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ekf2: add terrain state

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This commit is contained in:
bresch
2024-06-26 11:05:38 +02:00
committed by Silvan Fuhrer
parent 09f066a73a
commit 68980b59e2
53 changed files with 1936 additions and 1930 deletions
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msg/EstimatorAidSource1d.msg
-1
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@@ -25,4 +25,3 @@ bool fused # true if the sample was successfully fused
# TOPICS estimator_aid_src_airspeed estimator_aid_src_sideslip
# TOPICS estimator_aid_src_fake_hgt
# TOPICS estimator_aid_src_gnss_yaw estimator_aid_src_ev_yaw
# TOPICS estimator_aid_src_terrain_range_finder
msg/EstimatorAidSource2d.msg
+1 -1
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@@ -22,5 +22,5 @@ bool innovation_rejected # true if the observation has been rejected
bool fused # true if the sample was successfully fused
# TOPICS estimator_aid_src_ev_pos estimator_aid_src_fake_pos estimator_aid_src_gnss_pos estimator_aid_src_aux_global_position
# TOPICS estimator_aid_src_aux_vel estimator_aid_src_optical_flow estimator_aid_src_terrain_optical_flow
# TOPICS estimator_aid_src_aux_vel estimator_aid_src_optical_flow
# TOPICS estimator_aid_src_drag
msg/EstimatorBias.msg
+1 -1
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@@ -9,4 +9,4 @@ float32 innov # innovation of the last measurement fusion (m)
float32 innov_var # innovation variance of the last measurement fusion (m^2)
float32 innov_test_ratio # normalized innovation squared test ratio
# TOPICS estimator_baro_bias estimator_gnss_hgt_bias estimator_rng_hgt_bias
# TOPICS estimator_baro_bias estimator_gnss_hgt_bias
msg/EstimatorInnovations.msg
-1
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@@ -22,7 +22,6 @@ float32[2] aux_hvel # horizontal auxiliary velocity innovation from landing targ
# Optical flow
float32[2] flow # flow innvoation (rad/sec) and innovation variance ((rad/sec)**2)
float32[2] terr_flow # flow innvoation (rad/sec) and innovation variance computed by the terrain estimator ((rad/sec)**2)
# Various
float32 heading # heading innovation (rad) and innovation variance (rad**2)
msg/EstimatorStates.msg
+2 -2
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@@ -1,7 +1,7 @@
uint64 timestamp # time since system start (microseconds)
uint64 timestamp_sample # the timestamp of the raw data (microseconds)
float32[24] states # Internal filter states
float32[25] states # Internal filter states
uint8 n_states # Number of states effectively used
float32[23] covariances # Diagonal Elements of Covariance Matrix
float32[24] covariances # Diagonal Elements of Covariance Matrix
src/modules/ekf2/CMakeLists.txt
+1
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@@ -203,6 +203,7 @@ if(CONFIG_EKF2_RANGE_FINDER)
list(APPEND EKF_SRCS
EKF/aid_sources/range_finder/range_finder_consistency_check.cpp
EKF/aid_sources/range_finder/range_height_control.cpp
EKF/aid_sources/range_finder/range_height_fusion.cpp
EKF/aid_sources/range_finder/sensor_range_finder.cpp
)
endif()
src/modules/ekf2/EKF/CMakeLists.txt
+1
View File
@@ -125,6 +125,7 @@ if(CONFIG_EKF2_RANGE_FINDER)
list(APPEND EKF_SRCS
aid_sources/range_finder/range_finder_consistency_check.cpp
aid_sources/range_finder/range_height_control.cpp
aid_sources/range_finder/range_height_fusion.cpp
aid_sources/range_finder/sensor_range_finder.cpp
)
endif()
src/modules/ekf2/EKF/aid_sources/optical_flow/optical_flow_control.cpp
+47 -19
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@@ -55,13 +55,9 @@ void Ekf::controlOpticalFlowFusion(const imuSample &imu_delayed)
&& !_flow_sample_delayed.flow_rate.longerThan(_flow_max_rate);
const bool is_tilt_good = (_R_to_earth(2, 2) > _params.range_cos_max_tilt);
// don't enforce this condition if terrain estimate is not valid as we have logic below to coast through bad range finder data
const bool is_within_max_sensor_dist = isTerrainEstimateValid() ? (_terrain_vpos - _state.pos(2) <= _flow_max_distance) : true;
if (is_quality_good
&& is_magnitude_good
&& is_tilt_good
&& is_within_max_sensor_dist) {
&& is_tilt_good) {
// compensate for body motion to give a LOS rate
calcOptFlowBodyRateComp(imu_delayed);
_flow_rate_compensated = _flow_sample_delayed.flow_rate - _flow_sample_delayed.gyro_rate.xy();
@@ -81,20 +77,21 @@ void Ekf::controlOpticalFlowFusion(const imuSample &imu_delayed)
&& (_control_status.flags.inertial_dead_reckoning // is doing inertial dead-reckoning so must constrain drift urgently
|| isOnlyActiveSourceOfHorizontalAiding(_control_status.flags.opt_flow));
// Fuse optical flow LOS rate observations into the main filter only if height above ground has been updated recently
// use a relaxed time criteria to enable it to coast through bad range finder data
const bool terrain_available = isTerrainEstimateValid() || isRecent(_aid_src_terrain_range_finder.time_last_fuse, (uint64_t)10e6);
const bool is_within_max_sensor_dist = getHagl() <= _flow_max_distance;
const bool continuing_conditions_passing = (_params.flow_ctrl == 1)
&& _control_status.flags.tilt_align
&& (terrain_available || is_flow_required);
&& is_within_max_sensor_dist;
const bool starting_conditions_passing = continuing_conditions_passing
&& isTimedOut(_aid_src_optical_flow.time_last_fuse, (uint64_t)2e6); // Prevent rapid switching
// If the height is relative to the ground, terrain height cannot be observed.
_hagl_sensor_status.flags.flow = _control_status.flags.opt_flow && !(_height_sensor_ref == HeightSensor::RANGE);
if (_control_status.flags.opt_flow) {
if (continuing_conditions_passing) {
fuseOptFlow();
fuseOptFlow(_hagl_sensor_status.flags.flow);
// handle the case when we have optical flow, are reliant on it, but have not been using it for an extended period
if (isTimedOut(_aid_src_optical_flow.time_last_fuse, _params.no_aid_timeout_max)) {
@@ -125,19 +122,35 @@ void Ekf::startFlowFusion()
{
ECL_INFO("starting optical flow fusion");
if (!_aid_src_optical_flow.innovation_rejected && isHorizontalAidingActive()) {
// Consistent with the current velocity state, simply fuse the data without reset
fuseOptFlow();
_control_status.flags.opt_flow = true;
if (_height_sensor_ref != HeightSensor::RANGE) {
// If the height is relative to the ground, terrain height cannot be observed.
_hagl_sensor_status.flags.flow = true;
}
} else if (!isHorizontalAidingActive()) {
resetFlowFusion();
_control_status.flags.opt_flow = true;
if (isHorizontalAidingActive()) {
if (!_aid_src_optical_flow.innovation_rejected) {
ECL_INFO("starting optical flow no reset");
fuseOptFlow(_hagl_sensor_status.flags.flow);
} else if (_hagl_sensor_status.flags.flow && !_hagl_sensor_status.flags.range_finder) {
resetHaglFlow();
} else {
ECL_INFO("optical flow fusion failed to start");
_control_status.flags.opt_flow = false;
_hagl_sensor_status.flags.flow = false;
}
} else {
ECL_INFO("optical flow fusion failed to start");
_control_status.flags.opt_flow = false;
if (isTerrainEstimateValid() || (_height_sensor_ref == HeightSensor::RANGE)) {
resetFlowFusion();
} else if (_hagl_sensor_status.flags.flow) {
resetHaglFlow();
}
}
_control_status.flags.opt_flow = true; // needs to be here because of isHorizontalAidingActive
}
void Ekf::resetFlowFusion()
@@ -159,11 +172,26 @@ void Ekf::resetFlowFusion()
_aid_src_optical_flow.time_last_fuse = _time_delayed_us;
}
void Ekf::resetHaglFlow()
{
ECL_INFO("reset hagl to flow");
// TODO: use the flow data
_state.terrain = fmaxf(0.0f, _state.pos(2));
P.uncorrelateCovarianceSetVariance<State::terrain.dof>(State::terrain.idx, 100.f);
_terrain_vpos_reset_counter++;
_innov_check_fail_status.flags.reject_optflow_X = false;
_innov_check_fail_status.flags.reject_optflow_Y = false;
_aid_src_optical_flow.time_last_fuse = _time_delayed_us;
}
void Ekf::stopFlowFusion()
{
if (_control_status.flags.opt_flow) {
ECL_INFO("stopping optical flow fusion");
_control_status.flags.opt_flow = false;
_hagl_sensor_status.flags.flow = false;
}
}
src/modules/ekf2/EKF/aid_sources/optical_flow/optical_flow_fusion.cpp
+17 -6
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@@ -73,7 +73,7 @@ void Ekf::updateOptFlow(estimator_aid_source2d_s &aid_src)
Vector2f innov_var;
VectorState H;
sym::ComputeFlowXyInnovVarAndHx(_state.vector(), P, range, R_LOS, FLT_EPSILON, &innov_var, &H);
sym::ComputeFlowXyInnovVarAndHx(_state.vector(), P, R_LOS, FLT_EPSILON, &innov_var, &H);
// run the innovation consistency check and record result
updateAidSourceStatus(aid_src,
@@ -85,7 +85,7 @@ void Ekf::updateOptFlow(estimator_aid_source2d_s &aid_src)
math::max(_params.flow_innov_gate, 1.f)); // innovation gate
}
void Ekf::fuseOptFlow()
void Ekf::fuseOptFlow(const bool update_terrain)
{
const float R_LOS = _aid_src_optical_flow.observation_variance[0];
@@ -97,7 +97,7 @@ void Ekf::fuseOptFlow()
Vector2f innov_var;
VectorState H;
sym::ComputeFlowXyInnovVarAndHx(state_vector, P, range, R_LOS, FLT_EPSILON, &innov_var, &H);
sym::ComputeFlowXyInnovVarAndHx(state_vector, P, R_LOS, FLT_EPSILON, &innov_var, &H);
innov_var.copyTo(_aid_src_optical_flow.innovation_variance);
if ((innov_var(0) < R_LOS) || (innov_var(1) < R_LOS)) {
@@ -124,7 +124,7 @@ void Ekf::fuseOptFlow()
} else if (index == 1) {
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
sym::ComputeFlowYInnovVarAndH(state_vector, P, range, R_LOS, FLT_EPSILON, &_aid_src_optical_flow.innovation_variance[1], &H);
sym::ComputeFlowYInnovVarAndH(state_vector, P, R_LOS, FLT_EPSILON, &_aid_src_optical_flow.innovation_variance[1], &H);
// recalculate the innovation using the updated state
const Vector2f vel_body = predictFlowVelBody();
@@ -141,6 +141,10 @@ void Ekf::fuseOptFlow()
VectorState Kfusion = P * H / _aid_src_optical_flow.innovation_variance[index];
if (!update_terrain) {
Kfusion(State::terrain.idx) = 0.f;
}
if (measurementUpdate(Kfusion, H, _aid_src_optical_flow.observation_variance[index], _aid_src_optical_flow.innovation[index])) {
fused[index] = true;
}
@@ -165,10 +169,17 @@ float Ekf::predictFlowRange()
// calculate the height above the ground of the optical flow camera. Since earth frame is NED
// a positive offset in earth frame leads to a smaller height above the ground.
const float height_above_gnd_est = math::max(_terrain_vpos - _state.pos(2) - pos_offset_earth(2), fmaxf(_params.rng_gnd_clearance, 0.01f));
const float height_above_gnd_est = getHagl() - pos_offset_earth(2);
// calculate range from focal point to centre of image
return height_above_gnd_est / _R_to_earth(2, 2); // absolute distance to the frame region in view
float flow_range = height_above_gnd_est / _R_to_earth(2, 2); // absolute distance to the frame region in view
// avoid the flow prediction singularity at range = 0
if (fabsf(flow_range) < FLT_EPSILON) {
flow_range = signNoZero(flow_range) * FLT_EPSILON;
}
return flow_range;
}
Vector2f Ekf::predictFlowVelBody()
src/modules/ekf2/EKF/aid_sources/range_finder/range_height_control.cpp
+160 -96
View File
@@ -37,6 +37,7 @@
*/
#include "ekf.h"
#include "ekf_derivation/generated/compute_hagl_innov_var.h"
void Ekf::controlRangeHeightFusion()
{
@@ -93,58 +94,91 @@ void Ekf::controlRangeHeightFusion()
}
auto &aid_src = _aid_src_rng_hgt;
HeightBiasEstimator &bias_est = _rng_hgt_b_est;
bias_est.predict(_dt_ekf_avg);
if (rng_data_ready && _range_sensor.getSampleAddress()) {
const float measurement = math::max(_range_sensor.getDistBottom(), _params.rng_gnd_clearance);
const float measurement_var = sq(_params.range_noise) + sq(_params.range_noise_scaler * _range_sensor.getDistBottom());
updateRangeHeight(aid_src);
const bool measurement_valid = PX4_ISFINITE(aid_src.observation) && PX4_ISFINITE(aid_src.observation_variance);
const bool measurement_valid = PX4_ISFINITE(measurement) && PX4_ISFINITE(measurement_var);
// vertical position innovation - baro measurement has opposite sign to earth z axis
updateVerticalPositionAidStatus(aid_src,
_range_sensor.getSampleAddress()->time_us, // sample timestamp
-(measurement - bias_est.getBias()), // observation
measurement_var + bias_est.getBiasVar(), // observation variance
math::max(_params.range_innov_gate, 1.f)); // innovation gate
// update the bias estimator before updating the main filter but after
// using its current state to compute the vertical position innovation
if (measurement_valid && _range_sensor.isDataHealthy()) {
bias_est.setMaxStateNoise(sqrtf(measurement_var));
bias_est.setProcessNoiseSpectralDensity(_params.rng_hgt_bias_nsd);
bias_est.fuseBias(measurement - (-_state.pos(2)), measurement_var + P(State::pos.idx + 2, State::pos.idx + 2));
}
// determine if we should use height aiding
const bool do_conditional_range_aid = (_params.rng_ctrl == static_cast<int32_t>(RngCtrl::CONDITIONAL))
&& isConditionalRangeAidSuitable();
const bool continuing_conditions_passing = ((_params.rng_ctrl == static_cast<int32_t>(RngCtrl::ENABLED)) || do_conditional_range_aid)
&& measurement_valid
&& _range_sensor.isDataHealthy();
const bool continuing_conditions_passing = ((_params.rng_ctrl == static_cast<int32_t>(RngCtrl::ENABLED))
|| (_params.rng_ctrl == static_cast<int32_t>(RngCtrl::CONDITIONAL)))
&& _control_status.flags.tilt_align
&& measurement_valid
&& _range_sensor.isDataHealthy()
&& _rng_consistency_check.isKinematicallyConsistent();
const bool starting_conditions_passing = continuing_conditions_passing
&& isNewestSampleRecent(_time_last_range_buffer_push, 2 * estimator::sensor::RNG_MAX_INTERVAL)
&& _range_sensor.isRegularlySendingData();
const bool do_conditional_range_aid = (_hagl_sensor_status.flags.range_finder || _control_status.flags.rng_hgt)
&& (_params.rng_ctrl == static_cast<int32_t>(RngCtrl::CONDITIONAL))
&& isConditionalRangeAidSuitable();
const bool do_range_aid = (_hagl_sensor_status.flags.range_finder || _control_status.flags.rng_hgt)
&& (_params.rng_ctrl == static_cast<int32_t>(RngCtrl::ENABLED));
if (_control_status.flags.rng_hgt) {
if (!(do_conditional_range_aid || do_range_aid)) {
ECL_INFO("stopping %s fusion", HGT_SRC_NAME);
stopRngHgtFusion();
}
} else {
if (_params.height_sensor_ref == static_cast<int32_t>(HeightSensor::RANGE)) {
if (do_conditional_range_aid) {
// Range finder is used while hovering to stabilize the height estimate. Don't reset but use it as height reference.
ECL_INFO("starting conditional %s height fusion", HGT_SRC_NAME);
_height_sensor_ref = HeightSensor::RANGE;
_control_status.flags.rng_hgt = true;
stopRngTerrFusion();
if (!_hagl_sensor_status.flags.flow && aid_src.innovation_rejected) {
resetHaglRng(aid_src);
}
} else if (do_range_aid) {
// Range finder is the primary height source, the ground is now the datum used
// to compute the local vertical position
ECL_INFO("starting %s height fusion, resetting height", HGT_SRC_NAME);
_height_sensor_ref = HeightSensor::RANGE;
_information_events.flags.reset_hgt_to_rng = true;
resetVerticalPositionTo(-aid_src.observation, aid_src.observation_variance);
_state.terrain = 0.f;
_control_status.flags.rng_hgt = true;
stopRngTerrFusion();
aid_src.time_last_fuse = _time_delayed_us;
}
} else {
if (do_conditional_range_aid || do_range_aid) {
ECL_INFO("starting %s height fusion", HGT_SRC_NAME);
_control_status.flags.rng_hgt = true;
if (!_hagl_sensor_status.flags.flow && aid_src.innovation_rejected) {
resetHaglRng(aid_src);
}
}
}
}
if (_control_status.flags.rng_hgt || _hagl_sensor_status.flags.range_finder) {
if (continuing_conditions_passing) {
fuseVerticalPosition(aid_src);
fuseHaglRng(aid_src, _control_status.flags.rng_hgt, _hagl_sensor_status.flags.range_finder);
const bool is_fusion_failing = isTimedOut(aid_src.time_last_fuse, _params.hgt_fusion_timeout_max);
if (isHeightResetRequired()) {
if (isHeightResetRequired() && _control_status.flags.rng_hgt) {
// All height sources are failing
ECL_WARN("%s height fusion reset required, all height sources failing", HGT_SRC_NAME);
_information_events.flags.reset_hgt_to_rng = true;
resetVerticalPositionTo(-(measurement - bias_est.getBias()));
bias_est.setBias(_state.pos(2) + measurement);
resetVerticalPositionTo(-(aid_src.observation - _state.terrain));
// reset vertical velocity
resetVerticalVelocityToZero();
@@ -153,96 +187,123 @@ void Ekf::controlRangeHeightFusion()
} else if (is_fusion_failing) {
// Some other height source is still working
ECL_WARN("stopping %s height fusion, fusion failing", HGT_SRC_NAME);
stopRngHgtFusion();
_control_status.flags.rng_fault = true;
_range_sensor.setFaulty();
if (_hagl_sensor_status.flags.flow) {
ECL_WARN("stopping %s fusion, fusion failing", HGT_SRC_NAME);
stopRngHgtFusion();
stopRngTerrFusion();
} else {
resetHaglRng(aid_src);
}
}
} else {
ECL_WARN("stopping %s height fusion, continuing conditions failing", HGT_SRC_NAME);
ECL_WARN("stopping %s fusion, continuing conditions failing", HGT_SRC_NAME);
stopRngHgtFusion();
stopRngTerrFusion();
}
} else {
if (starting_conditions_passing) {
if ((_params.height_sensor_ref == static_cast<int32_t>(HeightSensor::RANGE))
&& (_params.rng_ctrl == static_cast<int32_t>(RngCtrl::CONDITIONAL))
) {
// Range finder is used while hovering to stabilize the height estimate. Don't reset but use it as height reference.
ECL_INFO("starting conditional %s height fusion", HGT_SRC_NAME);
_height_sensor_ref = HeightSensor::RANGE;
bias_est.setBias(_state.pos(2) + measurement);
} else if ((_params.height_sensor_ref == static_cast<int32_t>(HeightSensor::RANGE))
&& (_params.rng_ctrl != static_cast<int32_t>(RngCtrl::CONDITIONAL))
) {
// Range finder is the primary height source, the ground is now the datum used
// to compute the local vertical position
ECL_INFO("starting %s height fusion, resetting height", HGT_SRC_NAME);
_height_sensor_ref = HeightSensor::RANGE;
_information_events.flags.reset_hgt_to_rng = true;
resetVerticalPositionTo(-measurement, measurement_var);
bias_est.reset();
if (_hagl_sensor_status.flags.flow) {
if (!aid_src.innovation_rejected) {
_hagl_sensor_status.flags.range_finder = true;
fuseHaglRng(aid_src, _control_status.flags.rng_hgt, _hagl_sensor_status.flags.range_finder);
}
} else {
ECL_INFO("starting %s height fusion", HGT_SRC_NAME);
bias_est.setBias(_state.pos(2) + measurement);
}
if (aid_src.innovation_rejected) {
resetHaglRng(aid_src);
}
aid_src.time_last_fuse = _time_delayed_us;
bias_est.setFusionActive();
_control_status.flags.rng_hgt = true;
_hagl_sensor_status.flags.range_finder = true;
}
}
}
} else if (_control_status.flags.rng_hgt
} else if ((_control_status.flags.rng_hgt || _hagl_sensor_status.flags.range_finder)
&& !isNewestSampleRecent(_time_last_range_buffer_push, 2 * estimator::sensor::RNG_MAX_INTERVAL)) {
// No data anymore. Stop until it comes back.
ECL_WARN("stopping %s height fusion, no data", HGT_SRC_NAME);
ECL_WARN("stopping %s fusion, no data", HGT_SRC_NAME);
stopRngHgtFusion();
stopRngTerrFusion();
}
}
void Ekf::updateRangeHeight(estimator_aid_source1d_s &aid_src)
{
aid_src.observation = math::max(_range_sensor.getDistBottom(), _params.rng_gnd_clearance);
aid_src.innovation = getHagl() - aid_src.observation;
const float observation_variance = getRngVar();
float innovation_variance;
sym::ComputeHaglInnovVar(P, observation_variance, &innovation_variance);
const float innov_gate = math::max(_params.range_innov_gate, 1.f);
updateAidSourceStatus(aid_src,
_range_sensor.getSampleAddress()->time_us, // sample timestamp
math::max(_range_sensor.getDistBottom(), _params.rng_gnd_clearance), // observation
observation_variance, // observation variance
getHagl() - aid_src.observation, // innovation
innovation_variance, // innovation variance
math::max(_params.range_innov_gate, 1.f)); // innovation gate
// z special case if there is bad vertical acceleration data, then don't reject measurement,
// but limit innovation to prevent spikes that could destabilise the filter
if (_fault_status.flags.bad_acc_vertical && aid_src.innovation_rejected) {
const float innov_limit = innov_gate * sqrtf(aid_src.innovation_variance);
aid_src.innovation = math::constrain(aid_src.innovation, -innov_limit, innov_limit);
aid_src.innovation_rejected = false;
}
}
float Ekf::getRngVar() const
{
return fmaxf(
P(State::pos.idx + 2, State::pos.idx + 2)
+ sq(_params.range_noise)
+ sq(_params.range_noise_scaler * _range_sensor.getRange()),
0.f);
}
void Ekf::resetHaglRng(estimator_aid_source1d_s &aid_src)
{
_state.terrain = _state.pos(2) + aid_src.observation;
P.uncorrelateCovarianceSetVariance<State::terrain.dof>(State::terrain.idx, aid_src.observation_variance);
_terrain_vpos_reset_counter++;
aid_src.time_last_fuse = _time_delayed_us;
}
bool Ekf::isConditionalRangeAidSuitable()
{
#if defined(CONFIG_EKF2_TERRAIN)
if (_control_status.flags.in_air
&& _range_sensor.isHealthy()
&& isTerrainEstimateValid()) {
// check if we can use range finder measurements to estimate height, use hysteresis to avoid rapid switching
// Note that the 0.7 coefficients and the innovation check are arbitrary values but work well in practice
float range_hagl_max = _params.max_hagl_for_range_aid;
float max_vel_xy = _params.max_vel_for_range_aid;
// check if we can use range finder measurements to estimate height, use hysteresis to avoid rapid switching
// Note that the 0.7 coefficients and the innovation check are arbitrary values but work well in practice
float range_hagl_max = _params.max_hagl_for_range_aid;
float max_vel_xy = _params.max_vel_for_range_aid;
const float hagl_innov = _aid_src_terrain_range_finder.innovation;
const float hagl_innov_var = _aid_src_terrain_range_finder.innovation_variance;
const float hagl_test_ratio = _aid_src_rng_hgt.test_ratio;
const float hagl_test_ratio = (hagl_innov * hagl_innov / (sq(_params.range_aid_innov_gate) * hagl_innov_var));
bool is_hagl_stable = (hagl_test_ratio < 1.f);
bool is_hagl_stable = (hagl_test_ratio < 1.f);
if (!_control_status.flags.rng_hgt) {
range_hagl_max = 0.7f * _params.max_hagl_for_range_aid;
max_vel_xy = 0.7f * _params.max_vel_for_range_aid;
is_hagl_stable = (hagl_test_ratio < 0.01f);
}
const float range_hagl = _terrain_vpos - _state.pos(2);
const bool is_in_range = (range_hagl < range_hagl_max);
bool is_below_max_speed = true;
if (isHorizontalAidingActive()) {
is_below_max_speed = !_state.vel.xy().longerThan(max_vel_xy);
}
return is_in_range && is_hagl_stable && is_below_max_speed;
if (!_control_status.flags.rng_hgt) {
range_hagl_max = 0.7f * _params.max_hagl_for_range_aid;
max_vel_xy = 0.7f * _params.max_vel_for_range_aid;
is_hagl_stable = (hagl_test_ratio < 0.01f);
}
const bool is_in_range = (getHagl() < range_hagl_max);
bool is_below_max_speed = true;
if (isHorizontalAidingActive()) {
is_below_max_speed = !_state.vel.xy().longerThan(max_vel_xy);
}
return is_in_range && is_hagl_stable && is_below_max_speed;
#endif // CONFIG_EKF2_TERRAIN
return false;
@@ -256,8 +317,11 @@ void Ekf::stopRngHgtFusion()
_height_sensor_ref = HeightSensor::UNKNOWN;
}
_rng_hgt_b_est.setFusionInactive();
_control_status.flags.rng_hgt = false;
}
}
void Ekf::stopRngTerrFusion()
{
_hagl_sensor_status.flags.range_finder = false;
}
src/modules/ekf2/EKF/aid_sources/range_finder/range_height_fusion.cpp
+70
View File
@@ -0,0 +1,70 @@
/****************************************************************************
*
* Copyright (c) 2024 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
* 3. Neither the name PX4 nor the names of its contributors may be
* used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
****************************************************************************/
#include "ekf.h"
#include "ekf_derivation/generated/compute_hagl_h.h"
bool Ekf::fuseHaglRng(estimator_aid_source1d_s &aid_src, bool update_height, bool update_terrain)
{
if (aid_src.innovation_rejected) {
_innov_check_fail_status.flags.reject_hagl = true;
return false;
}
VectorState H;
sym::ComputeHaglH(&H);
// calculate the Kalman gain
VectorState K = P * H / aid_src.innovation_variance;
if (!update_terrain) {
K(State::terrain.idx) = 0.f;
}
if (!update_height) {
const float k_terrain = K(State::terrain.idx);
K.zero();
K(State::terrain.idx) = k_terrain;
}
measurementUpdate(K, H, aid_src.observation_variance, aid_src.innovation);
// record last successful fusion event
_innov_check_fail_status.flags.reject_hagl = false;
aid_src.time_last_fuse = _time_delayed_us;
aid_src.fused = true;
return true;
}
src/modules/ekf2/EKF/common.h
+4 -6
View File
@@ -107,7 +107,7 @@ enum MagFuseType : uint8_t {
#endif // CONFIG_EKF2_MAGNETOMETER
#if defined(CONFIG_EKF2_TERRAIN)
enum TerrainFusionMask : uint8_t {
enum class TerrainFusionMask : uint8_t {
TerrainFuseRangeFinder = (1 << 0),
TerrainFuseOpticalFlow = (1 << 1)
};
@@ -382,8 +382,8 @@ struct parameters {
#endif // CONFIG_EKF2_SIDESLIP
#if defined(CONFIG_EKF2_TERRAIN)
int32_t terrain_fusion_mode{TerrainFusionMask::TerrainFuseRangeFinder |
TerrainFusionMask::TerrainFuseOpticalFlow}; ///< aiding source(s) selection bitmask for the terrain estimator
int32_t terrain_fusion_mode{static_cast<int32_t>(TerrainFusionMask::TerrainFuseRangeFinder)
| static_cast<int32_t>(TerrainFusionMask::TerrainFuseOpticalFlow)}; ///< aiding source(s) selection bitmask for the terrain estimation
float terrain_p_noise{5.0f}; ///< process noise for terrain offset (m/sec)
float terrain_gradient{0.5f}; ///< gradient of terrain used to estimate process noise due to changing position (m/m)
@@ -401,10 +401,8 @@ struct parameters {
float range_delay_ms{5.0f}; ///< range finder measurement delay relative to the IMU (mSec)
float range_noise{0.1f}; ///< observation noise for range finder measurements (m)
float range_innov_gate{5.0f}; ///< range finder fusion innovation consistency gate size (STD)
float rng_hgt_bias_nsd{0.13f}; ///< process noise for range height bias estimation (m/s/sqrt(Hz))
float rng_sens_pitch{0.0f}; ///< Pitch offset of the range sensor (rad). Sensor points out along Z axis when offset is zero. Positive rotation is RH about Y axis.
float range_noise_scaler{0.0f}; ///< scaling from range measurement to noise (m/m)
const float vehicle_variance_scaler{0.0f}; ///< gain applied to vehicle height variance used in calculation of height above ground observation variance
float max_hagl_for_range_aid{5.0f}; ///< maximum height above ground for which we allow to use the range finder as height source (if rng_control == 1)
float max_vel_for_range_aid{1.0f}; ///< maximum ground velocity for which we allow to use the range finder as height source (if rng_control == 1)
float range_aid_innov_gate{1.0f}; ///< gate size used for innovation consistency checks for range aid fusion
@@ -521,7 +519,7 @@ union innovation_fault_status_u {
bool reject_yaw : 1; ///< 7 - true if the yaw observation has been rejected
bool reject_airspeed : 1; ///< 8 - true if the airspeed observation has been rejected
bool reject_sideslip : 1; ///< 9 - true if the synthetic sideslip observation has been rejected
bool reject_hagl : 1; ///< 10 - true if the height above ground observation has been rejected
bool reject_hagl : 1; ///< 10 - unused
bool reject_optflow_X : 1; ///< 11 - true if the X optical flow observation has been rejected
bool reject_optflow_Y : 1; ///< 12 - true if the Y optical flow observation has been rejected
} flags;
src/modules/ekf2/EKF/covariance.cpp
+15
View File
@@ -202,6 +202,17 @@ void Ekf::predictCovariance(const imuSample &imu_delayed)
}
#endif // CONFIG_EKF2_WIND
#if defined(CONFIG_EKF2_TERRAIN)
if (_height_sensor_ref != HeightSensor::RANGE) {
// predict the state variance growth where the state is the vertical position of the terrain underneath the vehicle
// process noise due to errors in vehicle height estimate
float terrain_process_noise = sq(imu_delayed.delta_vel_dt * _params.terrain_p_noise);
// process noise due to terrain gradient
terrain_process_noise += sq(imu_delayed.delta_vel_dt * _params.terrain_gradient) * (sq(_state.vel(0)) + sq(_state.vel(1)));
P(State::terrain.idx, State::terrain.idx) += terrain_process_noise;
}
#endif // CONFIG_EKF2_TERRAIN
// covariance matrix is symmetrical, so copy upper half to lower half
for (unsigned row = 0; row < State::size; row++) {
@@ -239,6 +250,10 @@ void Ekf::constrainStateVariances()
constrainStateVarLimitRatio(State::wind_vel, 1e-6f, 1e6f);
}
#endif // CONFIG_EKF2_WIND
#if defined(CONFIG_EKF2_TERRAIN)
constrainStateVarLimitRatio(State::terrain, 0.f, 1e4f);
#endif // CONFIG_EKF2_TERRAIN
}
void Ekf::constrainStateVar(const IdxDof &state, float min, float max)
src/modules/ekf2/EKF/ekf.cpp
+9 -2
View File
@@ -161,7 +161,6 @@ bool Ekf::update()
controlFusionModes(imu_sample_delayed);
#if defined(CONFIG_EKF2_TERRAIN)
// run a separate filter for terrain estimation
runTerrainEstimator(imu_sample_delayed);
#endif // CONFIG_EKF2_TERRAIN
@@ -201,7 +200,7 @@ bool Ekf::initialiseFilter()
initialiseCovariance();
#if defined(CONFIG_EKF2_TERRAIN)
// Initialise the terrain estimator
// Initialise the terrain state
initHagl();
#endif // CONFIG_EKF2_TERRAIN
@@ -377,6 +376,14 @@ void Ekf::print_status()
);
#endif // CONFIG_EKF2_WIND
#if defined(CONFIG_EKF2_TERRAIN)
printf("Terrain position (%d): %.3f var: %.1e\n",
State::terrain.idx,
(double)_state.terrain,
(double)getStateVariance<State::terrain>()(0)
);
#endif // CONFIG_EKF2_TERRAIN
printf("\nP:\n");
P.print();
src/modules/ekf2/EKF/ekf.h
+9 -36
View File
@@ -104,27 +104,19 @@ public:
uint8_t getTerrainEstimateSensorBitfield() const { return _hagl_sensor_status.value; }
// get the estimated terrain vertical position relative to the NED origin
float getTerrainVertPos() const { return _terrain_vpos; };
float getTerrainVertPos() const { return _state.terrain; };
float getHagl() const { return _state.terrain - _state.pos(2); }
// get the number of times the vertical terrain position has been reset
uint8_t getTerrainVertPosResetCounter() const { return _terrain_vpos_reset_counter; };
// get the terrain variance
float get_terrain_var() const { return _terrain_var; }
# if defined(CONFIG_EKF2_RANGE_FINDER)
const auto &aid_src_terrain_range_finder() const { return _aid_src_terrain_range_finder; }
# endif // CONFIG_EKF2_RANGE_FINDER
# if defined(CONFIG_EKF2_OPTICAL_FLOW)
const auto &aid_src_terrain_optical_flow() const { return _aid_src_terrain_optical_flow; }
# endif // CONFIG_EKF2_OPTICAL_FLOW
float getTerrainVariance() const { return P(State::terrain.idx, State::terrain.idx); }
#endif // CONFIG_EKF2_TERRAIN
#if defined(CONFIG_EKF2_RANGE_FINDER)
// range height
const BiasEstimator::status &getRngHgtBiasEstimatorStatus() const { return _rng_hgt_b_est.getStatus(); }
const auto &aid_src_rng_hgt() const { return _aid_src_rng_hgt; }
float getHaglRateInnov() const { return _rng_consistency_check.getInnov(); }
@@ -592,27 +584,14 @@ private:
#if defined(CONFIG_EKF2_TERRAIN)
// Terrain height state estimation
float _terrain_vpos{0.0f}; ///< estimated vertical position of the terrain underneath the vehicle in local NED frame (m)
float _terrain_var{1e4f}; ///< variance of terrain position estimate (m**2)
uint8_t _terrain_vpos_reset_counter{0}; ///< number of times _terrain_vpos has been reset
terrain_fusion_status_u _hagl_sensor_status{}; ///< Struct indicating type of sensor used to estimate height above ground
float _last_on_ground_posD{0.0f}; ///< last vertical position when the in_air status was false (m)
# if defined(CONFIG_EKF2_RANGE_FINDER)
estimator_aid_source1d_s _aid_src_terrain_range_finder{};
uint64_t _time_last_healthy_rng_data{0};
# endif // CONFIG_EKF2_RANGE_FINDER
# if defined(CONFIG_EKF2_OPTICAL_FLOW)
estimator_aid_source2d_s _aid_src_terrain_optical_flow{};
# endif // CONFIG_EKF2_OPTICAL_FLOW
#endif // CONFIG_EKF2_TERRAIN
#if defined(CONFIG_EKF2_RANGE_FINDER)
estimator_aid_source1d_s _aid_src_rng_hgt{};
HeightBiasEstimator _rng_hgt_b_est{HeightSensor::RANGE, _height_sensor_ref};
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
@@ -831,29 +810,22 @@ private:
// terrain vertical position estimator
void initHagl();
void runTerrainEstimator(const imuSample &imu_delayed);
void predictHagl(const imuSample &imu_delayed);
float getTerrainVPos() const { return isTerrainEstimateValid() ? _terrain_vpos : _last_on_ground_posD; }
float getTerrainVPos() const { return isTerrainEstimateValid() ? _state.terrain : _last_on_ground_posD; }
void controlHaglFakeFusion();
void terrainHandleVerticalPositionReset(float delta_z);
# if defined(CONFIG_EKF2_RANGE_FINDER)
// update the terrain vertical position estimate using a height above ground measurement from the range finder
void controlHaglRngFusion();
void updateHaglRng(estimator_aid_source1d_s &aid_src) const;
void fuseHaglRng(estimator_aid_source1d_s &aid_src);
void resetHaglRng();
void stopHaglRngFusion();
bool fuseHaglRng(estimator_aid_source1d_s &aid_src, bool update_height, bool update_terrain);
void updateRangeHeight(estimator_aid_source1d_s &aid_src);
void resetHaglRng(estimator_aid_source1d_s &aid_src);
float getRngVar() const;
# endif // CONFIG_EKF2_RANGE_FINDER
# if defined(CONFIG_EKF2_OPTICAL_FLOW)
// update the terrain vertical position estimate using an optical flow measurement
void controlHaglFlowFusion();
void resetHaglFlow();
void stopHaglFlowFusion();
void fuseFlowForTerrain(estimator_aid_source2d_s &flow);
# endif // CONFIG_EKF2_OPTICAL_FLOW
#endif // CONFIG_EKF2_TERRAIN
@@ -863,6 +835,7 @@ private:
void controlRangeHeightFusion();
bool isConditionalRangeAidSuitable();
void stopRngHgtFusion();
void stopRngTerrFusion();
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
@@ -883,7 +856,7 @@ private:
// fuse optical flow line of sight rate measurements
void updateOptFlow(estimator_aid_source2d_s &aid_src);
void fuseOptFlow();
void fuseOptFlow(bool update_terrain);
float predictFlowRange();
Vector2f predictFlowVelBody();
#endif // CONFIG_EKF2_OPTICAL_FLOW
src/modules/ekf2/EKF/ekf_helper.cpp
+9 -14
View File
@@ -211,7 +211,7 @@ void Ekf::get_ekf_vel_accuracy(float *ekf_evh, float *ekf_evv) const
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
if (_control_status.flags.opt_flow) {
float gndclearance = math::max(_params.rng_gnd_clearance, 0.1f);
vel_err_conservative = math::max((_terrain_vpos - _state.pos(2)), gndclearance) * Vector2f(_aid_src_optical_flow.innovation).norm();
vel_err_conservative = math::max(getHagl(), gndclearance) * Vector2f(_aid_src_optical_flow.innovation).norm();
}
#endif // CONFIG_EKF2_OPTICAL_FLOW
@@ -271,7 +271,7 @@ void Ekf::get_ekf_ctrl_limits(float *vxy_max, float *vz_max, float *hagl_min, fl
const float flow_hagl_min = fmaxf(rangefinder_hagl_min, _flow_min_distance);
const float flow_hagl_max = fminf(rangefinder_hagl_max, _flow_max_distance);
const float flow_constrained_height = math::constrain(_terrain_vpos - _state.pos(2), flow_hagl_min, flow_hagl_max);
const float flow_constrained_height = math::constrain(getHagl(), flow_hagl_min, flow_hagl_max);
// Allow ground relative velocity to use 50% of available flow sensor range to allow for angular motion
const float flow_vxy_max = 0.5f * _flow_max_rate * flow_constrained_height;
@@ -472,18 +472,9 @@ float Ekf::getHeightAboveGroundInnovationTestRatio() const
# if defined(CONFIG_EKF2_RANGE_FINDER)
if (_hagl_sensor_status.flags.range_finder) {
// return the terrain height innovation test ratio
test_ratio = math::max(test_ratio, fabsf(_aid_src_terrain_range_finder.test_ratio_filtered));
test_ratio = math::max(test_ratio, fabsf(_aid_src_rng_hgt.test_ratio_filtered));
}
#endif // CONFIG_EKF2_RANGE_FINDER
# if defined(CONFIG_EKF2_OPTICAL_FLOW)
if (_hagl_sensor_status.flags.flow) {
// return the terrain height innovation test ratio
for (auto &test_ratio_filtered : _aid_src_terrain_optical_flow.test_ratio_filtered) {
test_ratio = math::max(test_ratio, fabsf(test_ratio_filtered));
}
}
# endif // CONFIG_EKF2_OPTICAL_FLOW
# endif // CONFIG_EKF2_RANGE_FINDER
#endif // CONFIG_EKF2_TERRAIN
if (PX4_ISFINITE(test_ratio) && (test_ratio >= 0.f)) {
@@ -569,6 +560,10 @@ void Ekf::fuse(const VectorState &K, float innovation)
_state.wind_vel = matrix::constrain(_state.wind_vel - K.slice<State::wind_vel.dof, 1>(State::wind_vel.idx, 0) * innovation, -1.e2f, 1.e2f);
}
#endif // CONFIG_EKF2_WIND
#if defined(CONFIG_EKF2_TERRAIN)
_state.terrain = math::constrain(_state.terrain - K(State::terrain.idx) * innovation, -1e4f, 1e4f);
#endif // CONFIG_EKF2_TERRAIN
}
void Ekf::updateDeadReckoningStatus()
@@ -670,7 +665,7 @@ void Ekf::updateGroundEffect()
#if defined(CONFIG_EKF2_TERRAIN)
if (isTerrainEstimateValid()) {
// automatically set ground effect if terrain is valid
float height = _terrain_vpos - _state.pos(2);
float height = getHagl();
_control_status.flags.gnd_effect = (height < _params.gnd_effect_max_hgt);
} else
src/modules/ekf2/EKF/height_control.cpp
+2 -1
View File
@@ -301,7 +301,8 @@ Likelihood Ekf::estimateInertialNavFallingLikelihood() const
#if defined(CONFIG_EKF2_RANGE_FINDER)
if (_control_status.flags.rng_hgt) {
checks[3] = {ReferenceType::GROUND, _aid_src_rng_hgt.innovation, _aid_src_rng_hgt.innovation_variance};
// Range is a distance to ground measurement, not a direct height observation and has an opposite sign
checks[3] = {ReferenceType::GROUND, -_aid_src_rng_hgt.innovation, _aid_src_rng_hgt.innovation_variance};
}
#endif // CONFIG_EKF2_RANGE_FINDER
src/modules/ekf2/EKF/position_fusion.cpp
+1 -4
View File
@@ -161,12 +161,9 @@ void Ekf::resetVerticalPositionTo(const float new_vert_pos, float new_vert_pos_v
#if defined(CONFIG_EKF2_GNSS)
_gps_hgt_b_est.setBias(_gps_hgt_b_est.getBias() + delta_z);
#endif // CONFIG_EKF2_GNSS
#if defined(CONFIG_EKF2_RANGE_FINDER)
_rng_hgt_b_est.setBias(_rng_hgt_b_est.getBias() + delta_z);
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_TERRAIN)
terrainHandleVerticalPositionReset(delta_z);
_state.terrain += delta_z;
#endif
// Reset the timout timer
src/modules/ekf2/EKF/python/ekf_derivation/derivation.py
+40 -11
View File
@@ -68,7 +68,8 @@ State = Values(
accel_bias = sf.V3(),
mag_I = sf.V3(),
mag_B = sf.V3(),
wind_vel = sf.V2()
wind_vel = sf.V2(),
terrain = sf.V1()
)
if args.disable_mag:
@@ -132,7 +133,8 @@ def predict_covariance(
accel_bias = sf.V3.symbolic("delta_a_b"),
mag_I = sf.V3.symbolic("mag_I"),
mag_B = sf.V3.symbolic("mag_B"),
wind_vel = sf.V2.symbolic("wind_vel")
wind_vel = sf.V2.symbolic("wind_vel"),
terrain = sf.V1.symbolic("terrain")
)
if args.disable_mag:
@@ -456,7 +458,10 @@ def compute_mag_declination_pred_innov_var_and_h(
return (meas_pred, innov_var, H.T)
def predict_opt_flow(state, distance, epsilon):
def predict_hagl(state):
return state["terrain"][0] - state["pos"][2]
def predict_opt_flow(state, epsilon):
R_to_body = state["quat_nominal"].inverse()
# Calculate earth relative velocity in a non-rotating sensor frame
@@ -464,23 +469,23 @@ def predict_opt_flow(state, distance, epsilon):
# Divide by range to get predicted angular LOS rates relative to X and Y
# axes. Note these are rates in a non-rotating sensor frame
hagl = predict_hagl(state)
hagl = add_epsilon_sign(hagl, hagl, epsilon)
R_to_earth = state["quat_nominal"].to_rotation_matrix()
flow_pred = sf.V2()
flow_pred[0] = rel_vel_sensor[1] / distance
flow_pred[1] = -rel_vel_sensor[0] / distance
flow_pred = add_epsilon_sign(flow_pred, distance, epsilon)
flow_pred[0] = rel_vel_sensor[1] / hagl * R_to_earth[2, 2]
flow_pred[1] = -rel_vel_sensor[0] / hagl * R_to_earth[2, 2]
return flow_pred
def compute_flow_xy_innov_var_and_hx(
state: VState,
P: MTangent,
distance: sf.Scalar,
R: sf.Scalar,
epsilon: sf.Scalar
) -> (sf.V2, VTangent):
state = vstate_to_state(state)
meas_pred = predict_opt_flow(state, distance, epsilon);
meas_pred = predict_opt_flow(state, epsilon)
innov_var = sf.V2()
Hx = jacobian_chain_rule(meas_pred[0], state)
@@ -493,18 +498,40 @@ def compute_flow_xy_innov_var_and_hx(
def compute_flow_y_innov_var_and_h(
state: VState,
P: MTangent,
distance: sf.Scalar,
R: sf.Scalar,
epsilon: sf.Scalar
) -> (sf.Scalar, VTangent):
state = vstate_to_state(state)
meas_pred = predict_opt_flow(state, distance, epsilon);
meas_pred = predict_opt_flow(state, epsilon)
Hy = jacobian_chain_rule(meas_pred[1], state)
innov_var = (Hy * P * Hy.T + R)[0,0]
return (innov_var, Hy.T)
def compute_hagl_innov_var(
P: MTangent,
R: sf.Scalar,
) -> (sf.Scalar):
state = VState.symbolic("state")
state = vstate_to_state(state)
meas_pred = predict_hagl(state)
H = jacobian_chain_rule(meas_pred, state)
innov_var = (H * P * H.T + R)[0,0]
return (innov_var)
def compute_hagl_h(
) -> (VTangent):
state = VState.symbolic("state")
state = vstate_to_state(state)
meas_pred = predict_hagl(state)
H = jacobian_chain_rule(meas_pred, state)
return (H.T)
def compute_gnss_yaw_pred_innov_var_and_h(
state: VState,
P: MTangent,
@@ -664,6 +691,8 @@ if not args.disable_wind:
generate_px4_function(compute_yaw_innov_var_and_h, output_names=["innov_var", "H"])
generate_px4_function(compute_flow_xy_innov_var_and_hx, output_names=["innov_var", "H"])
generate_px4_function(compute_flow_y_innov_var_and_h, output_names=["innov_var", "H"])
generate_px4_function(compute_hagl_innov_var, output_names=["innov_var"])
generate_px4_function(compute_hagl_h, output_names=["H"])
generate_px4_function(compute_gnss_yaw_pred_innov_var_and_h, output_names=["meas_pred", "innov_var", "H"])
generate_px4_function(compute_gravity_xyz_innov_var_and_hx, output_names=["innov_var", "Hx"])
generate_px4_function(compute_gravity_y_innov_var_and_h, output_names=["innov_var", "Hy"])
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_airspeed_h_and_k.h
+13 -11
View File
@@ -16,21 +16,21 @@ namespace sym {
* Symbolic function: compute_airspeed_h_and_k
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* innov_var: Scalar
* epsilon: Scalar
*
* Outputs:
* H: Matrix23_1
* K: Matrix23_1
* H: Matrix24_1
* K: Matrix24_1
*/
template <typename Scalar>
void ComputeAirspeedHAndK(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar innov_var,
const Scalar epsilon, matrix::Matrix<Scalar, 23, 1>* const H = nullptr,
matrix::Matrix<Scalar, 23, 1>* const K = nullptr) {
// Total ops: 246
void ComputeAirspeedHAndK(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar innov_var,
const Scalar epsilon, matrix::Matrix<Scalar, 24, 1>* const H = nullptr,
matrix::Matrix<Scalar, 24, 1>* const K = nullptr) {
// Total ops: 256
// Input arrays
@@ -47,7 +47,7 @@ void ComputeAirspeedHAndK(const matrix::Matrix<Scalar, 24, 1>& state,
// Output terms (2)
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
@@ -59,7 +59,7 @@ void ComputeAirspeedHAndK(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (K != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _k = (*K);
matrix::Matrix<Scalar, 24, 1>& _k = (*K);
_k(0, 0) = _tmp6 * (-P(0, 21) * _tmp3 - P(0, 22) * _tmp4 + P(0, 3) * _tmp3 + P(0, 4) * _tmp4 +
P(0, 5) * _tmp5);
@@ -107,6 +107,8 @@ void ComputeAirspeedHAndK(const matrix::Matrix<Scalar, 24, 1>& state,
P(21, 4) * _tmp4 + P(21, 5) * _tmp5);
_k(22, 0) = _tmp6 * (-P(22, 21) * _tmp3 - P(22, 22) * _tmp4 + P(22, 3) * _tmp3 +
P(22, 4) * _tmp4 + P(22, 5) * _tmp5);
_k(23, 0) = _tmp6 * (-P(23, 21) * _tmp3 - P(23, 22) * _tmp4 + P(23, 3) * _tmp3 +
P(23, 4) * _tmp4 + P(23, 5) * _tmp5);
}
} // NOLINT(readability/fn_size)
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_airspeed_innov_and_innov_var.h
+4 -4
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: compute_airspeed_innov_and_innov_var
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* airspeed: Scalar
* R: Scalar
* epsilon: Scalar
@@ -27,8 +27,8 @@ namespace sym {
* innov_var: Scalar
*/
template <typename Scalar>
void ComputeAirspeedInnovAndInnovVar(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar airspeed,
void ComputeAirspeedInnovAndInnovVar(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar airspeed,
const Scalar R, const Scalar epsilon,
Scalar* const innov = nullptr,
Scalar* const innov_var = nullptr) {
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_drag_x_innov_var_and_h.h
+7 -7
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: compute_drag_x_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* rho: Scalar
* cd: Scalar
* cm: Scalar
@@ -26,14 +26,14 @@ namespace sym {
*
* Outputs:
* innov_var: Scalar
* Hx: Matrix23_1
* Hx: Matrix24_1
*/
template <typename Scalar>
void ComputeDragXInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar rho,
void ComputeDragXInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar rho,
const Scalar cd, const Scalar cm, const Scalar R,
const Scalar epsilon, Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const Hx = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const Hx = nullptr) {
// Total ops: 357
// Input arrays
@@ -162,7 +162,7 @@ void ComputeDragXInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (Hx != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _hx = (*Hx);
matrix::Matrix<Scalar, 24, 1>& _hx = (*Hx);
_hx.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_drag_y_innov_var_and_h.h
+7 -7
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: compute_drag_y_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* rho: Scalar
* cd: Scalar
* cm: Scalar
@@ -26,14 +26,14 @@ namespace sym {
*
* Outputs:
* innov_var: Scalar
* Hy: Matrix23_1
* Hy: Matrix24_1
*/
template <typename Scalar>
void ComputeDragYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar rho,
void ComputeDragYInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar rho,
const Scalar cd, const Scalar cm, const Scalar R,
const Scalar epsilon, Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const Hy = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const Hy = nullptr) {
// Total ops: 360
// Input arrays
@@ -162,7 +162,7 @@ void ComputeDragYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (Hy != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _hy = (*Hy);
matrix::Matrix<Scalar, 24, 1>& _hy = (*Hy);
_hy.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_flow_xy_innov_var_and_hx.h
+125 -89
View File
@@ -16,117 +16,153 @@ namespace sym {
* Symbolic function: compute_flow_xy_innov_var_and_hx
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* distance: Scalar
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
* epsilon: Scalar
*
* Outputs:
* innov_var: Matrix21
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeFlowXyInnovVarAndHx(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar distance,
const Scalar R, const Scalar epsilon,
void ComputeFlowXyInnovVarAndHx(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
const Scalar epsilon,
matrix::Matrix<Scalar, 2, 1>* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
// Total ops: 275
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 431
// Input arrays
// Intermediate terms (42)
const Scalar _tmp0 = 2 * state(4, 0);
const Scalar _tmp1 = 2 * state(1, 0);
const Scalar _tmp2 = _tmp1 * state(6, 0);
const Scalar _tmp3 = -_tmp0 * state(3, 0) + _tmp2;
const Scalar _tmp4 =
Scalar(1.0) /
(distance + epsilon * (2 * math::min<Scalar>(0, (((distance) > 0) - ((distance) < 0))) + 1));
const Scalar _tmp5 = (Scalar(1) / Scalar(2)) * _tmp4;
const Scalar _tmp6 = _tmp5 * state(2, 0);
const Scalar _tmp7 = 2 * state(3, 0) * state(6, 0);
const Scalar _tmp8 = _tmp5 * (_tmp0 * state(1, 0) + _tmp7);
const Scalar _tmp9 = 2 * state(0, 0);
const Scalar _tmp10 = state(3, 0) * state(5, 0);
const Scalar _tmp11 = 2 * state(2, 0);
const Scalar _tmp12 = _tmp11 * state(6, 0);
const Scalar _tmp13 = -4 * _tmp10 + _tmp12 - _tmp9 * state(4, 0);
const Scalar _tmp14 = _tmp5 * state(1, 0);
const Scalar _tmp15 = _tmp9 * state(6, 0);
const Scalar _tmp16 = _tmp0 * state(2, 0) + _tmp15 - 4 * state(1, 0) * state(5, 0);
const Scalar _tmp17 = _tmp5 * state(3, 0);
const Scalar _tmp18 = -_tmp13 * _tmp14 + _tmp16 * _tmp17 - _tmp3 * _tmp6 + _tmp8 * state(0, 0);
const Scalar _tmp19 = 1 - 2 * std::pow(state(3, 0), Scalar(2));
const Scalar _tmp20 = _tmp4 * (_tmp19 - 2 * std::pow(state(1, 0), Scalar(2)));
const Scalar _tmp21 = _tmp3 * _tmp5;
const Scalar _tmp22 = _tmp5 * state(0, 0);
const Scalar _tmp23 =
_tmp13 * _tmp6 + _tmp16 * _tmp22 - _tmp21 * state(1, 0) - _tmp8 * state(3, 0);
const Scalar _tmp24 =
_tmp13 * _tmp22 - _tmp16 * _tmp6 - _tmp21 * state(3, 0) + _tmp8 * state(1, 0);
const Scalar _tmp25 = _tmp9 * state(3, 0);
const Scalar _tmp26 = _tmp1 * state(2, 0);
const Scalar _tmp27 = _tmp4 * (-_tmp25 + _tmp26);
const Scalar _tmp28 = _tmp4 * (_tmp11 * state(3, 0) + _tmp9 * state(1, 0));
const Scalar _tmp29 = _tmp4 * (_tmp19 - 2 * std::pow(state(2, 0), Scalar(2)));
const Scalar _tmp30 = 4 * state(4, 0);
const Scalar _tmp31 = _tmp2 - _tmp30 * state(3, 0) + _tmp9 * state(5, 0);
const Scalar _tmp32 = 2 * state(5, 0);
const Scalar _tmp33 = -_tmp15 - _tmp30 * state(2, 0) + _tmp32 * state(1, 0);
const Scalar _tmp34 = _tmp32 * state(2, 0) + _tmp7;
const Scalar _tmp35 = 2 * _tmp10 - _tmp12;
const Scalar _tmp36 = _tmp35 * _tmp5;
const Scalar _tmp37 = _tmp17 * _tmp33 - _tmp22 * _tmp34 - _tmp31 * _tmp6 + _tmp36 * state(1, 0);
const Scalar _tmp38 = -_tmp14 * _tmp33 - _tmp22 * _tmp31 + _tmp34 * _tmp6 + _tmp36 * state(3, 0);
const Scalar _tmp39 = _tmp14 * _tmp31 - _tmp17 * _tmp34 - _tmp22 * _tmp33 + _tmp35 * _tmp6;
const Scalar _tmp40 = _tmp4 * (_tmp25 + _tmp26);
const Scalar _tmp41 = _tmp4 * (_tmp1 * state(3, 0) - _tmp9 * state(2, 0));
// Intermediate terms (66)
const Scalar _tmp0 = state(2, 0) * state(4, 0);
const Scalar _tmp1 = state(1, 0) * state(5, 0);
const Scalar _tmp2 = 2 * state(6, 0);
const Scalar _tmp3 = _tmp2 * state(0, 0);
const Scalar _tmp4 = -2 * std::pow(state(2, 0), Scalar(2));
const Scalar _tmp5 = 1 - 2 * std::pow(state(1, 0), Scalar(2));
const Scalar _tmp6 = _tmp4 + _tmp5;
const Scalar _tmp7 = state(24, 0) - state(9, 0);
const Scalar _tmp8 =
_tmp7 + epsilon * (2 * math::min<Scalar>(0, (((_tmp7) > 0) - ((_tmp7) < 0))) + 1);
const Scalar _tmp9 = Scalar(1.0) / (_tmp8);
const Scalar _tmp10 = _tmp6 * _tmp9;
const Scalar _tmp11 = 2 * state(0, 0) * state(3, 0);
const Scalar _tmp12 = 2 * state(1, 0);
const Scalar _tmp13 = _tmp12 * state(2, 0);
const Scalar _tmp14 = -_tmp11 + _tmp13;
const Scalar _tmp15 = 2 * state(2, 0);
const Scalar _tmp16 = _tmp12 * state(0, 0) + _tmp15 * state(3, 0);
const Scalar _tmp17 = -2 * std::pow(state(3, 0), Scalar(2));
const Scalar _tmp18 = _tmp17 + _tmp5;
const Scalar _tmp19 = _tmp14 * state(4, 0) + _tmp16 * state(6, 0) + _tmp18 * state(5, 0);
const Scalar _tmp20 = 4 * _tmp9;
const Scalar _tmp21 = _tmp19 * _tmp20;
const Scalar _tmp22 = _tmp10 * (2 * _tmp0 - 4 * _tmp1 + _tmp3) - _tmp21 * state(1, 0);
const Scalar _tmp23 = (Scalar(1) / Scalar(2)) * _tmp22;
const Scalar _tmp24 = 2 * state(4, 0);
const Scalar _tmp25 = 4 * state(3, 0);
const Scalar _tmp26 = _tmp2 * state(2, 0);
const Scalar _tmp27 = -_tmp24 * state(0, 0) - _tmp25 * state(5, 0) + _tmp26;
const Scalar _tmp28 = (Scalar(1) / Scalar(2)) * _tmp10;
const Scalar _tmp29 = _tmp28 * state(0, 0);
const Scalar _tmp30 = (Scalar(1) / Scalar(2)) * state(3, 0);
const Scalar _tmp31 = _tmp2 * state(1, 0);
const Scalar _tmp32 = _tmp10 * (-_tmp24 * state(3, 0) + _tmp31);
const Scalar _tmp33 = _tmp2 * state(3, 0);
const Scalar _tmp34 = _tmp10 * (_tmp24 * state(1, 0) + _tmp33) - _tmp21 * state(2, 0);
const Scalar _tmp35 = (Scalar(1) / Scalar(2)) * _tmp34;
const Scalar _tmp36 =
-_tmp23 * state(2, 0) + _tmp27 * _tmp29 - _tmp30 * _tmp32 + _tmp35 * state(1, 0);
const Scalar _tmp37 = _tmp10 * _tmp14;
const Scalar _tmp38 = _tmp28 * state(2, 0);
const Scalar _tmp39 = (Scalar(1) / Scalar(2)) * _tmp32;
const Scalar _tmp40 =
_tmp23 * state(0, 0) + _tmp27 * _tmp38 - _tmp30 * _tmp34 - _tmp39 * state(1, 0);
const Scalar _tmp41 = _tmp28 * state(1, 0);
const Scalar _tmp42 =
_tmp22 * _tmp30 - _tmp27 * _tmp41 + _tmp35 * state(0, 0) - _tmp39 * state(2, 0);
const Scalar _tmp43 = _tmp10 * _tmp18;
const Scalar _tmp44 = _tmp6 / std::pow(_tmp8, Scalar(2));
const Scalar _tmp45 = _tmp19 * _tmp44;
const Scalar _tmp46 = _tmp10 * _tmp16;
const Scalar _tmp47 = _tmp12 * state(3, 0) - _tmp15 * state(0, 0);
const Scalar _tmp48 = _tmp10 * _tmp47;
const Scalar _tmp49 = _tmp11 + _tmp13;
const Scalar _tmp50 = _tmp10 * _tmp49;
const Scalar _tmp51 = _tmp17 + _tmp4 + 1;
const Scalar _tmp52 = _tmp10 * _tmp51;
const Scalar _tmp53 = _tmp47 * state(6, 0) + _tmp49 * state(5, 0) + _tmp51 * state(4, 0);
const Scalar _tmp54 = _tmp20 * _tmp53;
const Scalar _tmp55 = 2 * state(5, 0);
const Scalar _tmp56 = -_tmp10 * (_tmp33 + _tmp55 * state(2, 0)) + _tmp54 * state(1, 0);
const Scalar _tmp57 = -_tmp10 * (-4 * _tmp0 + 2 * _tmp1 - _tmp3) + _tmp54 * state(2, 0);
const Scalar _tmp58 = (Scalar(1) / Scalar(2)) * _tmp57;
const Scalar _tmp59 = -_tmp25 * state(4, 0) + _tmp31 + _tmp55 * state(0, 0);
const Scalar _tmp60 = -_tmp26 + _tmp55 * state(3, 0);
const Scalar _tmp61 = _tmp30 * _tmp56 + _tmp38 * _tmp60 + _tmp41 * _tmp59 + _tmp58 * state(0, 0);
const Scalar _tmp62 = (Scalar(1) / Scalar(2)) * _tmp56;
const Scalar _tmp63 = -_tmp30 * _tmp57 - _tmp38 * _tmp59 + _tmp41 * _tmp60 + _tmp62 * state(0, 0);
const Scalar _tmp64 =
_tmp10 * _tmp30 * _tmp60 - _tmp29 * _tmp59 + _tmp58 * state(1, 0) - _tmp62 * state(2, 0);
const Scalar _tmp65 = _tmp44 * _tmp53;
// Output terms (2)
if (innov_var != nullptr) {
matrix::Matrix<Scalar, 2, 1>& _innov_var = (*innov_var);
_innov_var(0, 0) = R +
_tmp18 * (P(0, 1) * _tmp23 + P(1, 1) * _tmp18 + P(2, 1) * _tmp24 +
P(3, 1) * _tmp27 + P(4, 1) * _tmp20 + P(5, 1) * _tmp28) +
_tmp20 * (P(0, 4) * _tmp23 + P(1, 4) * _tmp18 + P(2, 4) * _tmp24 +
P(3, 4) * _tmp27 + P(4, 4) * _tmp20 + P(5, 4) * _tmp28) +
_tmp23 * (P(0, 0) * _tmp23 + P(1, 0) * _tmp18 + P(2, 0) * _tmp24 +
P(3, 0) * _tmp27 + P(4, 0) * _tmp20 + P(5, 0) * _tmp28) +
_tmp24 * (P(0, 2) * _tmp23 + P(1, 2) * _tmp18 + P(2, 2) * _tmp24 +
P(3, 2) * _tmp27 + P(4, 2) * _tmp20 + P(5, 2) * _tmp28) +
_tmp27 * (P(0, 3) * _tmp23 + P(1, 3) * _tmp18 + P(2, 3) * _tmp24 +
P(3, 3) * _tmp27 + P(4, 3) * _tmp20 + P(5, 3) * _tmp28) +
_tmp28 * (P(0, 5) * _tmp23 + P(1, 5) * _tmp18 + P(2, 5) * _tmp24 +
P(3, 5) * _tmp27 + P(4, 5) * _tmp20 + P(5, 5) * _tmp28);
_innov_var(1, 0) = R -
_tmp29 * (P(0, 3) * _tmp37 + P(1, 3) * _tmp39 + P(2, 3) * _tmp38 -
P(3, 3) * _tmp29 - P(4, 3) * _tmp40 - P(5, 3) * _tmp41) +
_tmp37 * (P(0, 0) * _tmp37 + P(1, 0) * _tmp39 + P(2, 0) * _tmp38 -
P(3, 0) * _tmp29 - P(4, 0) * _tmp40 - P(5, 0) * _tmp41) +
_tmp38 * (P(0, 2) * _tmp37 + P(1, 2) * _tmp39 + P(2, 2) * _tmp38 -
P(3, 2) * _tmp29 - P(4, 2) * _tmp40 - P(5, 2) * _tmp41) +
_tmp39 * (P(0, 1) * _tmp37 + P(1, 1) * _tmp39 + P(2, 1) * _tmp38 -
P(3, 1) * _tmp29 - P(4, 1) * _tmp40 - P(5, 1) * _tmp41) -
_tmp40 * (P(0, 4) * _tmp37 + P(1, 4) * _tmp39 + P(2, 4) * _tmp38 -
P(3, 4) * _tmp29 - P(4, 4) * _tmp40 - P(5, 4) * _tmp41) -
_tmp41 * (P(0, 5) * _tmp37 + P(1, 5) * _tmp39 + P(2, 5) * _tmp38 -
P(3, 5) * _tmp29 - P(4, 5) * _tmp40 - P(5, 5) * _tmp41);
_innov_var(0, 0) =
R +
_tmp36 * (P(0, 2) * _tmp40 + P(1, 2) * _tmp42 + P(2, 2) * _tmp36 - P(23, 2) * _tmp45 +
P(3, 2) * _tmp37 + P(4, 2) * _tmp43 + P(5, 2) * _tmp46 + P(8, 2) * _tmp45) +
_tmp37 * (P(0, 3) * _tmp40 + P(1, 3) * _tmp42 + P(2, 3) * _tmp36 - P(23, 3) * _tmp45 +
P(3, 3) * _tmp37 + P(4, 3) * _tmp43 + P(5, 3) * _tmp46 + P(8, 3) * _tmp45) +
_tmp40 * (P(0, 0) * _tmp40 + P(1, 0) * _tmp42 + P(2, 0) * _tmp36 - P(23, 0) * _tmp45 +
P(3, 0) * _tmp37 + P(4, 0) * _tmp43 + P(5, 0) * _tmp46 + P(8, 0) * _tmp45) +
_tmp42 * (P(0, 1) * _tmp40 + P(1, 1) * _tmp42 + P(2, 1) * _tmp36 - P(23, 1) * _tmp45 +
P(3, 1) * _tmp37 + P(4, 1) * _tmp43 + P(5, 1) * _tmp46 + P(8, 1) * _tmp45) +
_tmp43 * (P(0, 4) * _tmp40 + P(1, 4) * _tmp42 + P(2, 4) * _tmp36 - P(23, 4) * _tmp45 +
P(3, 4) * _tmp37 + P(4, 4) * _tmp43 + P(5, 4) * _tmp46 + P(8, 4) * _tmp45) -
_tmp45 * (P(0, 23) * _tmp40 + P(1, 23) * _tmp42 + P(2, 23) * _tmp36 - P(23, 23) * _tmp45 +
P(3, 23) * _tmp37 + P(4, 23) * _tmp43 + P(5, 23) * _tmp46 + P(8, 23) * _tmp45) +
_tmp45 * (P(0, 8) * _tmp40 + P(1, 8) * _tmp42 + P(2, 8) * _tmp36 - P(23, 8) * _tmp45 +
P(3, 8) * _tmp37 + P(4, 8) * _tmp43 + P(5, 8) * _tmp46 + P(8, 8) * _tmp45) +
_tmp46 * (P(0, 5) * _tmp40 + P(1, 5) * _tmp42 + P(2, 5) * _tmp36 - P(23, 5) * _tmp45 +
P(3, 5) * _tmp37 + P(4, 5) * _tmp43 + P(5, 5) * _tmp46 + P(8, 5) * _tmp45);
_innov_var(1, 0) =
R -
_tmp48 * (P(0, 5) * _tmp63 + P(1, 5) * _tmp61 + P(2, 5) * _tmp64 + P(23, 5) * _tmp65 -
P(3, 5) * _tmp52 - P(4, 5) * _tmp50 - P(5, 5) * _tmp48 - P(8, 5) * _tmp65) -
_tmp50 * (P(0, 4) * _tmp63 + P(1, 4) * _tmp61 + P(2, 4) * _tmp64 + P(23, 4) * _tmp65 -
P(3, 4) * _tmp52 - P(4, 4) * _tmp50 - P(5, 4) * _tmp48 - P(8, 4) * _tmp65) -
_tmp52 * (P(0, 3) * _tmp63 + P(1, 3) * _tmp61 + P(2, 3) * _tmp64 + P(23, 3) * _tmp65 -
P(3, 3) * _tmp52 - P(4, 3) * _tmp50 - P(5, 3) * _tmp48 - P(8, 3) * _tmp65) +
_tmp61 * (P(0, 1) * _tmp63 + P(1, 1) * _tmp61 + P(2, 1) * _tmp64 + P(23, 1) * _tmp65 -
P(3, 1) * _tmp52 - P(4, 1) * _tmp50 - P(5, 1) * _tmp48 - P(8, 1) * _tmp65) +
_tmp63 * (P(0, 0) * _tmp63 + P(1, 0) * _tmp61 + P(2, 0) * _tmp64 + P(23, 0) * _tmp65 -
P(3, 0) * _tmp52 - P(4, 0) * _tmp50 - P(5, 0) * _tmp48 - P(8, 0) * _tmp65) +
_tmp64 * (P(0, 2) * _tmp63 + P(1, 2) * _tmp61 + P(2, 2) * _tmp64 + P(23, 2) * _tmp65 -
P(3, 2) * _tmp52 - P(4, 2) * _tmp50 - P(5, 2) * _tmp48 - P(8, 2) * _tmp65) +
_tmp65 * (P(0, 23) * _tmp63 + P(1, 23) * _tmp61 + P(2, 23) * _tmp64 + P(23, 23) * _tmp65 -
P(3, 23) * _tmp52 - P(4, 23) * _tmp50 - P(5, 23) * _tmp48 - P(8, 23) * _tmp65) -
_tmp65 * (P(0, 8) * _tmp63 + P(1, 8) * _tmp61 + P(2, 8) * _tmp64 + P(23, 8) * _tmp65 -
P(3, 8) * _tmp52 - P(4, 8) * _tmp50 - P(5, 8) * _tmp48 - P(8, 8) * _tmp65);
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
_h(0, 0) = _tmp23;
_h(1, 0) = _tmp18;
_h(2, 0) = _tmp24;
_h(3, 0) = _tmp27;
_h(4, 0) = _tmp20;
_h(5, 0) = _tmp28;
_h(0, 0) = _tmp40;
_h(1, 0) = _tmp42;
_h(2, 0) = _tmp36;
_h(3, 0) = _tmp37;
_h(4, 0) = _tmp43;
_h(5, 0) = _tmp46;
_h(8, 0) = _tmp45;
_h(23, 0) = -_tmp45;
}
} // NOLINT(readability/fn_size)
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_flow_y_innov_var_and_h.h
+72 -57
View File
@@ -16,85 +16,100 @@ namespace sym {
* Symbolic function: compute_flow_y_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* distance: Scalar
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
* epsilon: Scalar
*
* Outputs:
* innov_var: Scalar
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeFlowYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar distance,
const Scalar R, const Scalar epsilon,
Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
// Total ops: 151
void ComputeFlowYInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
const Scalar epsilon, Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 232
// Input arrays
// Intermediate terms (21)
const Scalar _tmp0 =
Scalar(1.0) /
(distance + epsilon * (2 * math::min<Scalar>(0, (((distance) > 0) - ((distance) < 0))) + 1));
const Scalar _tmp1 =
_tmp0 * (-2 * std::pow(state(2, 0), Scalar(2)) - 2 * std::pow(state(3, 0), Scalar(2)) + 1);
const Scalar _tmp2 = 4 * state(4, 0);
const Scalar _tmp3 = 2 * state(0, 0);
const Scalar _tmp4 = 2 * state(6, 0);
const Scalar _tmp5 = -_tmp2 * state(3, 0) + _tmp3 * state(5, 0) + _tmp4 * state(1, 0);
const Scalar _tmp6 = (Scalar(1) / Scalar(2)) * _tmp0;
const Scalar _tmp7 = _tmp5 * _tmp6;
const Scalar _tmp8 = 2 * state(1, 0);
const Scalar _tmp9 = -_tmp2 * state(2, 0) - _tmp4 * state(0, 0) + _tmp8 * state(5, 0);
const Scalar _tmp10 = _tmp6 * _tmp9;
const Scalar _tmp11 = 2 * state(5, 0);
const Scalar _tmp12 = _tmp6 * (_tmp11 * state(2, 0) + _tmp4 * state(3, 0));
const Scalar _tmp13 = _tmp11 * state(3, 0) - _tmp4 * state(2, 0);
const Scalar _tmp14 = _tmp6 * state(1, 0);
const Scalar _tmp15 =
_tmp10 * state(3, 0) - _tmp12 * state(0, 0) + _tmp13 * _tmp14 - _tmp7 * state(2, 0);
const Scalar _tmp16 = _tmp13 * _tmp6;
const Scalar _tmp17 =
_tmp12 * state(2, 0) - _tmp14 * _tmp9 + _tmp16 * state(3, 0) - _tmp7 * state(0, 0);
// Intermediate terms (28)
const Scalar _tmp0 = 2 * state(0, 0);
const Scalar _tmp1 = 2 * state(1, 0);
const Scalar _tmp2 = -_tmp0 * state(2, 0) + _tmp1 * state(3, 0);
const Scalar _tmp3 = 1 - 2 * std::pow(state(2, 0), Scalar(2));
const Scalar _tmp4 = _tmp3 - 2 * std::pow(state(1, 0), Scalar(2));
const Scalar _tmp5 = state(24, 0) - state(9, 0);
const Scalar _tmp6 =
_tmp5 + epsilon * (2 * math::min<Scalar>(0, (((_tmp5) > 0) - ((_tmp5) < 0))) + 1);
const Scalar _tmp7 = Scalar(1.0) / (_tmp6);
const Scalar _tmp8 = _tmp4 * _tmp7;
const Scalar _tmp9 = _tmp2 * _tmp8;
const Scalar _tmp10 = _tmp0 * state(3, 0) + _tmp1 * state(2, 0);
const Scalar _tmp11 = _tmp10 * _tmp8;
const Scalar _tmp12 = _tmp3 - 2 * std::pow(state(3, 0), Scalar(2));
const Scalar _tmp13 = _tmp12 * _tmp8;
const Scalar _tmp14 = _tmp10 * state(5, 0) + _tmp12 * state(4, 0) + _tmp2 * state(6, 0);
const Scalar _tmp15 = 4 * _tmp14 * _tmp7;
const Scalar _tmp16 = 2 * state(5, 0);
const Scalar _tmp17 = 2 * state(6, 0);
const Scalar _tmp18 =
-_tmp10 * state(0, 0) - _tmp12 * state(3, 0) + _tmp14 * _tmp5 + _tmp16 * state(2, 0);
const Scalar _tmp19 = _tmp0 * (_tmp3 * state(3, 0) + _tmp8 * state(2, 0));
const Scalar _tmp20 = _tmp0 * (-_tmp3 * state(2, 0) + _tmp8 * state(3, 0));
(Scalar(1) / Scalar(2)) * _tmp15 * state(1, 0) -
Scalar(1) / Scalar(2) * _tmp8 * (_tmp16 * state(2, 0) + _tmp17 * state(3, 0));
const Scalar _tmp19 = 4 * state(4, 0);
const Scalar _tmp20 = (Scalar(1) / Scalar(2)) * _tmp15 * state(2, 0) -
Scalar(1) / Scalar(2) * _tmp8 *
(_tmp16 * state(1, 0) - _tmp17 * state(0, 0) - _tmp19 * state(2, 0));
const Scalar _tmp21 = (Scalar(1) / Scalar(2)) * _tmp8;
const Scalar _tmp22 =
_tmp21 * (_tmp16 * state(0, 0) + _tmp17 * state(1, 0) - _tmp19 * state(3, 0));
const Scalar _tmp23 = _tmp21 * (_tmp16 * state(3, 0) - _tmp17 * state(2, 0));
const Scalar _tmp24 =
_tmp18 * state(3, 0) + _tmp20 * state(0, 0) + _tmp22 * state(1, 0) + _tmp23 * state(2, 0);
const Scalar _tmp25 =
_tmp18 * state(0, 0) - _tmp20 * state(3, 0) - _tmp22 * state(2, 0) + _tmp23 * state(1, 0);
const Scalar _tmp26 =
-_tmp18 * state(2, 0) + _tmp20 * state(1, 0) - _tmp22 * state(0, 0) + _tmp23 * state(3, 0);
const Scalar _tmp27 = _tmp14 * _tmp4 / std::pow(_tmp6, Scalar(2));
// Output terms (2)
if (innov_var != nullptr) {
Scalar& _innov_var = (*innov_var);
_innov_var = R -
_tmp1 * (P(0, 3) * _tmp15 + P(1, 3) * _tmp18 + P(2, 3) * _tmp17 - P(3, 3) * _tmp1 -
P(4, 3) * _tmp19 - P(5, 3) * _tmp20) +
_tmp15 * (P(0, 0) * _tmp15 + P(1, 0) * _tmp18 + P(2, 0) * _tmp17 -
P(3, 0) * _tmp1 - P(4, 0) * _tmp19 - P(5, 0) * _tmp20) +
_tmp17 * (P(0, 2) * _tmp15 + P(1, 2) * _tmp18 + P(2, 2) * _tmp17 -
P(3, 2) * _tmp1 - P(4, 2) * _tmp19 - P(5, 2) * _tmp20) +
_tmp18 * (P(0, 1) * _tmp15 + P(1, 1) * _tmp18 + P(2, 1) * _tmp17 -
P(3, 1) * _tmp1 - P(4, 1) * _tmp19 - P(5, 1) * _tmp20) -
_tmp19 * (P(0, 4) * _tmp15 + P(1, 4) * _tmp18 + P(2, 4) * _tmp17 -
P(3, 4) * _tmp1 - P(4, 4) * _tmp19 - P(5, 4) * _tmp20) -
_tmp20 * (P(0, 5) * _tmp15 + P(1, 5) * _tmp18 + P(2, 5) * _tmp17 -
P(3, 5) * _tmp1 - P(4, 5) * _tmp19 - P(5, 5) * _tmp20);
_innov_var =
R -
_tmp11 * (P(0, 4) * _tmp25 + P(1, 4) * _tmp24 + P(2, 4) * _tmp26 + P(23, 4) * _tmp27 -
P(3, 4) * _tmp13 - P(4, 4) * _tmp11 - P(5, 4) * _tmp9 - P(8, 4) * _tmp27) -
_tmp13 * (P(0, 3) * _tmp25 + P(1, 3) * _tmp24 + P(2, 3) * _tmp26 + P(23, 3) * _tmp27 -
P(3, 3) * _tmp13 - P(4, 3) * _tmp11 - P(5, 3) * _tmp9 - P(8, 3) * _tmp27) +
_tmp24 * (P(0, 1) * _tmp25 + P(1, 1) * _tmp24 + P(2, 1) * _tmp26 + P(23, 1) * _tmp27 -
P(3, 1) * _tmp13 - P(4, 1) * _tmp11 - P(5, 1) * _tmp9 - P(8, 1) * _tmp27) +
_tmp25 * (P(0, 0) * _tmp25 + P(1, 0) * _tmp24 + P(2, 0) * _tmp26 + P(23, 0) * _tmp27 -
P(3, 0) * _tmp13 - P(4, 0) * _tmp11 - P(5, 0) * _tmp9 - P(8, 0) * _tmp27) +
_tmp26 * (P(0, 2) * _tmp25 + P(1, 2) * _tmp24 + P(2, 2) * _tmp26 + P(23, 2) * _tmp27 -
P(3, 2) * _tmp13 - P(4, 2) * _tmp11 - P(5, 2) * _tmp9 - P(8, 2) * _tmp27) +
_tmp27 * (P(0, 23) * _tmp25 + P(1, 23) * _tmp24 + P(2, 23) * _tmp26 + P(23, 23) * _tmp27 -
P(3, 23) * _tmp13 - P(4, 23) * _tmp11 - P(5, 23) * _tmp9 - P(8, 23) * _tmp27) -
_tmp27 * (P(0, 8) * _tmp25 + P(1, 8) * _tmp24 + P(2, 8) * _tmp26 + P(23, 8) * _tmp27 -
P(3, 8) * _tmp13 - P(4, 8) * _tmp11 - P(5, 8) * _tmp9 - P(8, 8) * _tmp27) -
_tmp9 * (P(0, 5) * _tmp25 + P(1, 5) * _tmp24 + P(2, 5) * _tmp26 + P(23, 5) * _tmp27 -
P(3, 5) * _tmp13 - P(4, 5) * _tmp11 - P(5, 5) * _tmp9 - P(8, 5) * _tmp27);
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
_h(0, 0) = _tmp15;
_h(1, 0) = _tmp18;
_h(2, 0) = _tmp17;
_h(3, 0) = -_tmp1;
_h(4, 0) = -_tmp19;
_h(5, 0) = -_tmp20;
_h(0, 0) = _tmp25;
_h(1, 0) = _tmp24;
_h(2, 0) = _tmp26;
_h(3, 0) = -_tmp13;
_h(4, 0) = -_tmp11;
_h(5, 0) = -_tmp9;
_h(8, 0) = -_tmp27;
_h(23, 0) = _tmp27;
}
} // NOLINT(readability/fn_size)
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_gnss_yaw_pred_innov_var_and_h.h
+7 -7
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: compute_gnss_yaw_pred_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* antenna_yaw_offset: Scalar
* R: Scalar
* epsilon: Scalar
@@ -25,15 +25,15 @@ namespace sym {
* Outputs:
* meas_pred: Scalar
* innov_var: Scalar
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeGnssYawPredInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P,
void ComputeGnssYawPredInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P,
const Scalar antenna_yaw_offset, const Scalar R,
const Scalar epsilon, Scalar* const meas_pred = nullptr,
Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 114
// Input arrays
@@ -93,7 +93,7 @@ void ComputeGnssYawPredInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_gravity_xyz_innov_var_and_hx.h
+7 -7
View File
@@ -16,19 +16,19 @@ namespace sym {
* Symbolic function: compute_gravity_xyz_innov_var_and_hx
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
*
* Outputs:
* innov_var: Matrix31
* Hx: Matrix23_1
* Hx: Matrix24_1
*/
template <typename Scalar>
void ComputeGravityXyzInnovVarAndHx(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeGravityXyzInnovVarAndHx(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
matrix::Matrix<Scalar, 3, 1>* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const Hx = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const Hx = nullptr) {
// Total ops: 53
// Input arrays
@@ -61,7 +61,7 @@ void ComputeGravityXyzInnovVarAndHx(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (Hx != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _hx = (*Hx);
matrix::Matrix<Scalar, 24, 1>& _hx = (*Hx);
_hx.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_gravity_y_innov_var_and_h.h
+7 -7
View File
@@ -16,19 +16,19 @@ namespace sym {
* Symbolic function: compute_gravity_y_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
*
* Outputs:
* innov_var: Scalar
* Hy: Matrix23_1
* Hy: Matrix24_1
*/
template <typename Scalar>
void ComputeGravityYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeGravityYInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const Hy = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const Hy = nullptr) {
// Total ops: 22
// Input arrays
@@ -47,7 +47,7 @@ void ComputeGravityYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (Hy != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _hy = (*Hy);
matrix::Matrix<Scalar, 24, 1>& _hy = (*Hy);
_hy.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_gravity_z_innov_var_and_h.h
+7 -7
View File
@@ -16,19 +16,19 @@ namespace sym {
* Symbolic function: compute_gravity_z_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
*
* Outputs:
* innov_var: Scalar
* Hz: Matrix23_1
* Hz: Matrix24_1
*/
template <typename Scalar>
void ComputeGravityZInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeGravityZInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const Hz = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const Hz = nullptr) {
// Total ops: 18
// Input arrays
@@ -48,7 +48,7 @@ void ComputeGravityZInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (Hz != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _hz = (*Hz);
matrix::Matrix<Scalar, 24, 1>& _hz = (*Hz);
_hz.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_hagl_h.h
+43
View File
@@ -0,0 +1,43 @@
// -----------------------------------------------------------------------------
// This file was autogenerated by symforce from template:
// function/FUNCTION.h.jinja
// Do NOT modify by hand.
// -----------------------------------------------------------------------------
#pragma once
#include <matrix/math.hpp>
namespace sym {
/**
* This function was autogenerated from a symbolic function. Do not modify by hand.
*
* Symbolic function: compute_hagl_h
*
* Args:
*
* Outputs:
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeHaglH(matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 0
// Input arrays
// Intermediate terms (0)
// Output terms (1)
if (H != nullptr) {
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
_h(8, 0) = -1;
_h(23, 0) = 1;
}
} // NOLINT(readability/fn_size)
// NOLINTNEXTLINE(readability/fn_size)
} // namespace sym
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_hagl_innov_var.h
+43
View File
@@ -0,0 +1,43 @@
// -----------------------------------------------------------------------------
// This file was autogenerated by symforce from template:
// function/FUNCTION.h.jinja
// Do NOT modify by hand.
// -----------------------------------------------------------------------------
#pragma once
#include <matrix/math.hpp>
namespace sym {
/**
* This function was autogenerated from a symbolic function. Do not modify by hand.
*
* Symbolic function: compute_hagl_innov_var
*
* Args:
* P: Matrix24_24
* R: Scalar
*
* Outputs:
* innov_var: Scalar
*/
template <typename Scalar>
void ComputeHaglInnovVar(const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
Scalar* const innov_var = nullptr) {
// Total ops: 4
// Input arrays
// Intermediate terms (0)
// Output terms (1)
if (innov_var != nullptr) {
Scalar& _innov_var = (*innov_var);
_innov_var = P(23, 23) - P(23, 8) - P(8, 23) + P(8, 8) + R;
}
} // NOLINT(readability/fn_size)
// NOLINTNEXTLINE(readability/fn_size)
} // namespace sym
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_mag_declination_pred_innov_var_and_h.h
+7 -7
View File
@@ -16,22 +16,22 @@ namespace sym {
* Symbolic function: compute_mag_declination_pred_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
* epsilon: Scalar
*
* Outputs:
* pred: Scalar
* innov_var: Scalar
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeMagDeclinationPredInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeMagDeclinationPredInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
const Scalar epsilon, Scalar* const pred = nullptr,
Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 22
// Input arrays
@@ -59,7 +59,7 @@ void ComputeMagDeclinationPredInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>&
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_mag_innov_innov_var_and_hx.h
+7 -7
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: compute_mag_innov_innov_var_and_hx
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* meas: Matrix31
* R: Scalar
* epsilon: Scalar
@@ -25,16 +25,16 @@ namespace sym {
* Outputs:
* innov: Matrix31
* innov_var: Matrix31
* Hx: Matrix23_1
* Hx: Matrix24_1
*/
template <typename Scalar>
void ComputeMagInnovInnovVarAndHx(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P,
void ComputeMagInnovInnovVarAndHx(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P,
const matrix::Matrix<Scalar, 3, 1>& meas, const Scalar R,
const Scalar epsilon,
matrix::Matrix<Scalar, 3, 1>* const innov = nullptr,
matrix::Matrix<Scalar, 3, 1>* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const Hx = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const Hx = nullptr) {
// Total ops: 461
// Unused inputs
@@ -185,7 +185,7 @@ void ComputeMagInnovInnovVarAndHx(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (Hx != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _hx = (*Hx);
matrix::Matrix<Scalar, 24, 1>& _hx = (*Hx);
_hx.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_mag_y_innov_var_and_h.h
+7 -7
View File
@@ -16,20 +16,20 @@ namespace sym {
* Symbolic function: compute_mag_y_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
* epsilon: Scalar
*
* Outputs:
* innov_var: Scalar
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeMagYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeMagYInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
const Scalar epsilon, Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 159
// Unused inputs
@@ -85,7 +85,7 @@ void ComputeMagYInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_mag_z_innov_var_and_h.h
+7 -7
View File
@@ -16,20 +16,20 @@ namespace sym {
* Symbolic function: compute_mag_z_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
* epsilon: Scalar
*
* Outputs:
* innov_var: Scalar
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeMagZInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeMagZInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
const Scalar epsilon, Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 161
// Unused inputs
@@ -85,7 +85,7 @@ void ComputeMagZInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_sideslip_h_and_k.h
+119 -116
View File
@@ -16,169 +16,172 @@ namespace sym {
* Symbolic function: compute_sideslip_h_and_k
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* innov_var: Scalar
* epsilon: Scalar
*
* Outputs:
* H: Matrix23_1
* K: Matrix23_1
* H: Matrix24_1
* K: Matrix24_1
*/
template <typename Scalar>
void ComputeSideslipHAndK(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar innov_var,
const Scalar epsilon, matrix::Matrix<Scalar, 23, 1>* const H = nullptr,
matrix::Matrix<Scalar, 23, 1>* const K = nullptr) {
// Total ops: 497
void ComputeSideslipHAndK(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar innov_var,
const Scalar epsilon, matrix::Matrix<Scalar, 24, 1>* const H = nullptr,
matrix::Matrix<Scalar, 24, 1>* const K = nullptr) {
// Total ops: 513
// Input arrays
// Intermediate terms (45)
// Intermediate terms (43)
const Scalar _tmp0 = -state(23, 0) + state(5, 0);
const Scalar _tmp1 = 2 * state(1, 0);
const Scalar _tmp2 = -state(22, 0) + state(4, 0);
const Scalar _tmp3 = 2 * state(6, 0);
const Scalar _tmp4 = _tmp3 * state(0, 0);
const Scalar _tmp5 = 1 - 2 * std::pow(state(3, 0), Scalar(2));
const Scalar _tmp6 = _tmp5 - 2 * std::pow(state(2, 0), Scalar(2));
const Scalar _tmp7 = 2 * state(0, 0);
const Scalar _tmp8 = _tmp7 * state(3, 0);
const Scalar _tmp9 = 2 * state(2, 0);
const Scalar _tmp10 = _tmp9 * state(1, 0);
const Scalar _tmp11 = _tmp10 + _tmp8;
const Scalar _tmp12 = _tmp1 * state(3, 0) - _tmp9 * state(0, 0);
const Scalar _tmp13 = _tmp0 * _tmp11 + _tmp12 * state(6, 0) + _tmp2 * _tmp6;
const Scalar _tmp14 =
_tmp13 + epsilon * (2 * math::min<Scalar>(0, (((_tmp13) > 0) - ((_tmp13) < 0))) + 1);
const Scalar _tmp15 = _tmp5 - 2 * std::pow(state(1, 0), Scalar(2));
const Scalar _tmp16 = _tmp10 - _tmp8;
const Scalar _tmp17 = _tmp7 * state(1, 0) + _tmp9 * state(3, 0);
const Scalar _tmp18 =
(_tmp0 * _tmp15 + _tmp16 * _tmp2 + _tmp17 * state(6, 0)) / std::pow(_tmp14, Scalar(2));
const Scalar _tmp19 = _tmp3 * state(3, 0);
const Scalar _tmp20 = Scalar(1.0) / (_tmp14);
const Scalar _tmp21 = -_tmp18 * (_tmp0 * _tmp1 - 4 * _tmp2 * state(2, 0) - _tmp4) +
_tmp20 * (_tmp1 * _tmp2 + _tmp19);
const Scalar _tmp22 = (Scalar(1) / Scalar(2)) * state(3, 0);
const Scalar _tmp23 =
-Scalar(1) / Scalar(2) * _tmp18 * (_tmp0 * _tmp9 + _tmp19) +
(Scalar(1) / Scalar(2)) * _tmp20 * (-4 * _tmp0 * state(1, 0) + _tmp2 * _tmp9 + _tmp4);
const Scalar _tmp24 = 2 * state(3, 0);
const Scalar _tmp25 = _tmp3 * state(2, 0);
const Scalar _tmp26 = _tmp3 * state(1, 0);
const Scalar _tmp27 = -_tmp18 * (_tmp0 * _tmp24 - _tmp25) + _tmp20 * (-_tmp2 * _tmp24 + _tmp26);
const Scalar _tmp28 = (Scalar(1) / Scalar(2)) * _tmp27;
const Scalar _tmp29 = 4 * state(3, 0);
const Scalar _tmp3 = 4 * _tmp2;
const Scalar _tmp4 = 2 * state(6, 0);
const Scalar _tmp5 = _tmp4 * state(0, 0);
const Scalar _tmp6 = 1 - 2 * std::pow(state(3, 0), Scalar(2));
const Scalar _tmp7 = _tmp6 - 2 * std::pow(state(2, 0), Scalar(2));
const Scalar _tmp8 = 2 * state(0, 0);
const Scalar _tmp9 = _tmp8 * state(3, 0);
const Scalar _tmp10 = 2 * state(2, 0);
const Scalar _tmp11 = _tmp10 * state(1, 0);
const Scalar _tmp12 = _tmp11 + _tmp9;
const Scalar _tmp13 = _tmp1 * state(3, 0) - _tmp10 * state(0, 0);
const Scalar _tmp14 = _tmp0 * _tmp12 + _tmp13 * state(6, 0) + _tmp2 * _tmp7;
const Scalar _tmp15 =
_tmp14 + epsilon * (2 * math::min<Scalar>(0, (((_tmp14) > 0) - ((_tmp14) < 0))) + 1);
const Scalar _tmp16 = _tmp6 - 2 * std::pow(state(1, 0), Scalar(2));
const Scalar _tmp17 = _tmp11 - _tmp9;
const Scalar _tmp18 = _tmp10 * state(3, 0) + _tmp8 * state(1, 0);
const Scalar _tmp19 =
(_tmp0 * _tmp16 + _tmp17 * _tmp2 + _tmp18 * state(6, 0)) / std::pow(_tmp15, Scalar(2));
const Scalar _tmp20 = _tmp4 * state(3, 0);
const Scalar _tmp21 = Scalar(1.0) / (_tmp15);
const Scalar _tmp22 =
-Scalar(1) / Scalar(2) * _tmp19 * (_tmp0 * _tmp1 - _tmp3 * state(2, 0) - _tmp5) +
(Scalar(1) / Scalar(2)) * _tmp21 * (_tmp1 * _tmp2 + _tmp20);
const Scalar _tmp23 = 4 * _tmp0;
const Scalar _tmp24 =
-Scalar(1) / Scalar(2) * _tmp19 * (_tmp0 * _tmp10 + _tmp20) +
(Scalar(1) / Scalar(2)) * _tmp21 * (_tmp10 * _tmp2 - _tmp23 * state(1, 0) + _tmp5);
const Scalar _tmp25 = 2 * state(3, 0);
const Scalar _tmp26 = _tmp4 * state(2, 0);
const Scalar _tmp27 = _tmp4 * state(1, 0);
const Scalar _tmp28 = -Scalar(1) / Scalar(2) * _tmp19 * (_tmp0 * _tmp25 - _tmp26) +
(Scalar(1) / Scalar(2)) * _tmp21 * (-_tmp2 * _tmp25 + _tmp27);
const Scalar _tmp29 =
-Scalar(1) / Scalar(2) * _tmp19 * (_tmp0 * _tmp8 + _tmp27 - _tmp3 * state(3, 0)) +
(Scalar(1) / Scalar(2)) * _tmp21 * (-_tmp2 * _tmp8 - _tmp23 * state(3, 0) + _tmp26);
const Scalar _tmp30 =
-Scalar(1) / Scalar(2) * _tmp18 * (_tmp0 * _tmp7 - _tmp2 * _tmp29 + _tmp26) +
(Scalar(1) / Scalar(2)) * _tmp20 * (-_tmp0 * _tmp29 - _tmp2 * _tmp7 + _tmp25);
-_tmp22 * state(3, 0) + _tmp24 * state(0, 0) - _tmp28 * state(1, 0) + _tmp29 * state(2, 0);
const Scalar _tmp31 =
-_tmp21 * _tmp22 + _tmp23 * state(0, 0) - _tmp28 * state(1, 0) + _tmp30 * state(2, 0);
const Scalar _tmp32 = (Scalar(1) / Scalar(2)) * _tmp21;
const Scalar _tmp33 =
_tmp23 * state(3, 0) - _tmp28 * state(2, 0) - _tmp30 * state(1, 0) + _tmp32 * state(0, 0);
const Scalar _tmp34 =
-_tmp22 * _tmp27 - _tmp23 * state(2, 0) + _tmp30 * state(0, 0) + _tmp32 * state(1, 0);
const Scalar _tmp35 = _tmp18 * _tmp6;
const Scalar _tmp36 = _tmp16 * _tmp20;
const Scalar _tmp37 = -_tmp35 + _tmp36;
const Scalar _tmp38 = _tmp11 * _tmp18;
const Scalar _tmp39 = _tmp15 * _tmp20;
const Scalar _tmp40 = -_tmp38 + _tmp39;
const Scalar _tmp41 = -_tmp12 * _tmp18 + _tmp17 * _tmp20;
const Scalar _tmp42 = _tmp35 - _tmp36;
const Scalar _tmp43 = _tmp38 - _tmp39;
const Scalar _tmp44 = Scalar(1.0) / (math::max<Scalar>(epsilon, innov_var));
_tmp22 * state(0, 0) + _tmp24 * state(3, 0) - _tmp28 * state(2, 0) - _tmp29 * state(1, 0);
const Scalar _tmp32 =
_tmp22 * state(1, 0) - _tmp24 * state(2, 0) - _tmp28 * state(3, 0) + _tmp29 * state(0, 0);
const Scalar _tmp33 = _tmp19 * _tmp7;
const Scalar _tmp34 = _tmp17 * _tmp21;
const Scalar _tmp35 = -_tmp33 + _tmp34;
const Scalar _tmp36 = _tmp12 * _tmp19;
const Scalar _tmp37 = _tmp16 * _tmp21;
const Scalar _tmp38 = -_tmp36 + _tmp37;
const Scalar _tmp39 = -_tmp13 * _tmp19 + _tmp18 * _tmp21;
const Scalar _tmp40 = _tmp33 - _tmp34;
const Scalar _tmp41 = _tmp36 - _tmp37;
const Scalar _tmp42 = Scalar(1.0) / (math::max<Scalar>(epsilon, innov_var));
// Output terms (2)
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
_h(0, 0) = _tmp31;
_h(1, 0) = _tmp33;
_h(2, 0) = _tmp34;
_h(3, 0) = _tmp37;
_h(4, 0) = _tmp40;
_h(5, 0) = _tmp41;
_h(21, 0) = _tmp42;
_h(22, 0) = _tmp43;
_h(0, 0) = _tmp30;
_h(1, 0) = _tmp31;
_h(2, 0) = _tmp32;
_h(3, 0) = _tmp35;
_h(4, 0) = _tmp38;
_h(5, 0) = _tmp39;
_h(21, 0) = _tmp40;
_h(22, 0) = _tmp41;
}
if (K != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _k = (*K);
matrix::Matrix<Scalar, 24, 1>& _k = (*K);
_k(0, 0) =
_tmp44 * (P(0, 0) * _tmp31 + P(0, 1) * _tmp33 + P(0, 2) * _tmp34 + P(0, 21) * _tmp42 +
P(0, 22) * _tmp43 + P(0, 3) * _tmp37 + P(0, 4) * _tmp40 + P(0, 5) * _tmp41);
_tmp42 * (P(0, 0) * _tmp30 + P(0, 1) * _tmp31 + P(0, 2) * _tmp32 + P(0, 21) * _tmp40 +
P(0, 22) * _tmp41 + P(0, 3) * _tmp35 + P(0, 4) * _tmp38 + P(0, 5) * _tmp39);
_k(1, 0) =
_tmp44 * (P(1, 0) * _tmp31 + P(1, 1) * _tmp33 + P(1, 2) * _tmp34 + P(1, 21) * _tmp42 +
P(1, 22) * _tmp43 + P(1, 3) * _tmp37 + P(1, 4) * _tmp40 + P(1, 5) * _tmp41);
_tmp42 * (P(1, 0) * _tmp30 + P(1, 1) * _tmp31 + P(1, 2) * _tmp32 + P(1, 21) * _tmp40 +
P(1, 22) * _tmp41 + P(1, 3) * _tmp35 + P(1, 4) * _tmp38 + P(1, 5) * _tmp39);
_k(2, 0) =
_tmp44 * (P(2, 0) * _tmp31 + P(2, 1) * _tmp33 + P(2, 2) * _tmp34 + P(2, 21) * _tmp42 +
P(2, 22) * _tmp43 + P(2, 3) * _tmp37 + P(2, 4) * _tmp40 + P(2, 5) * _tmp41);
_tmp42 * (P(2, 0) * _tmp30 + P(2, 1) * _tmp31 + P(2, 2) * _tmp32 + P(2, 21) * _tmp40 +
P(2, 22) * _tmp41 + P(2, 3) * _tmp35 + P(2, 4) * _tmp38 + P(2, 5) * _tmp39);
_k(3, 0) =
_tmp44 * (P(3, 0) * _tmp31 + P(3, 1) * _tmp33 + P(3, 2) * _tmp34 + P(3, 21) * _tmp42 +
P(3, 22) * _tmp43 + P(3, 3) * _tmp37 + P(3, 4) * _tmp40 + P(3, 5) * _tmp41);
_tmp42 * (P(3, 0) * _tmp30 + P(3, 1) * _tmp31 + P(3, 2) * _tmp32 + P(3, 21) * _tmp40 +
P(3, 22) * _tmp41 + P(3, 3) * _tmp35 + P(3, 4) * _tmp38 + P(3, 5) * _tmp39);
_k(4, 0) =
_tmp44 * (P(4, 0) * _tmp31 + P(4, 1) * _tmp33 + P(4, 2) * _tmp34 + P(4, 21) * _tmp42 +
P(4, 22) * _tmp43 + P(4, 3) * _tmp37 + P(4, 4) * _tmp40 + P(4, 5) * _tmp41);
_tmp42 * (P(4, 0) * _tmp30 + P(4, 1) * _tmp31 + P(4, 2) * _tmp32 + P(4, 21) * _tmp40 +
P(4, 22) * _tmp41 + P(4, 3) * _tmp35 + P(4, 4) * _tmp38 + P(4, 5) * _tmp39);
_k(5, 0) =
_tmp44 * (P(5, 0) * _tmp31 + P(5, 1) * _tmp33 + P(5, 2) * _tmp34 + P(5, 21) * _tmp42 +
P(5, 22) * _tmp43 + P(5, 3) * _tmp37 + P(5, 4) * _tmp40 + P(5, 5) * _tmp41);
_tmp42 * (P(5, 0) * _tmp30 + P(5, 1) * _tmp31 + P(5, 2) * _tmp32 + P(5, 21) * _tmp40 +
P(5, 22) * _tmp41 + P(5, 3) * _tmp35 + P(5, 4) * _tmp38 + P(5, 5) * _tmp39);
_k(6, 0) =
_tmp44 * (P(6, 0) * _tmp31 + P(6, 1) * _tmp33 + P(6, 2) * _tmp34 + P(6, 21) * _tmp42 +
P(6, 22) * _tmp43 + P(6, 3) * _tmp37 + P(6, 4) * _tmp40 + P(6, 5) * _tmp41);
_tmp42 * (P(6, 0) * _tmp30 + P(6, 1) * _tmp31 + P(6, 2) * _tmp32 + P(6, 21) * _tmp40 +
P(6, 22) * _tmp41 + P(6, 3) * _tmp35 + P(6, 4) * _tmp38 + P(6, 5) * _tmp39);
_k(7, 0) =
_tmp44 * (P(7, 0) * _tmp31 + P(7, 1) * _tmp33 + P(7, 2) * _tmp34 + P(7, 21) * _tmp42 +
P(7, 22) * _tmp43 + P(7, 3) * _tmp37 + P(7, 4) * _tmp40 + P(7, 5) * _tmp41);
_tmp42 * (P(7, 0) * _tmp30 + P(7, 1) * _tmp31 + P(7, 2) * _tmp32 + P(7, 21) * _tmp40 +
P(7, 22) * _tmp41 + P(7, 3) * _tmp35 + P(7, 4) * _tmp38 + P(7, 5) * _tmp39);
_k(8, 0) =
_tmp44 * (P(8, 0) * _tmp31 + P(8, 1) * _tmp33 + P(8, 2) * _tmp34 + P(8, 21) * _tmp42 +
P(8, 22) * _tmp43 + P(8, 3) * _tmp37 + P(8, 4) * _tmp40 + P(8, 5) * _tmp41);
_tmp42 * (P(8, 0) * _tmp30 + P(8, 1) * _tmp31 + P(8, 2) * _tmp32 + P(8, 21) * _tmp40 +
P(8, 22) * _tmp41 + P(8, 3) * _tmp35 + P(8, 4) * _tmp38 + P(8, 5) * _tmp39);
_k(9, 0) =
_tmp44 * (P(9, 0) * _tmp31 + P(9, 1) * _tmp33 + P(9, 2) * _tmp34 + P(9, 21) * _tmp42 +
P(9, 22) * _tmp43 + P(9, 3) * _tmp37 + P(9, 4) * _tmp40 + P(9, 5) * _tmp41);
_tmp42 * (P(9, 0) * _tmp30 + P(9, 1) * _tmp31 + P(9, 2) * _tmp32 + P(9, 21) * _tmp40 +
P(9, 22) * _tmp41 + P(9, 3) * _tmp35 + P(9, 4) * _tmp38 + P(9, 5) * _tmp39);
_k(10, 0) =
_tmp44 * (P(10, 0) * _tmp31 + P(10, 1) * _tmp33 + P(10, 2) * _tmp34 + P(10, 21) * _tmp42 +
P(10, 22) * _tmp43 + P(10, 3) * _tmp37 + P(10, 4) * _tmp40 + P(10, 5) * _tmp41);
_tmp42 * (P(10, 0) * _tmp30 + P(10, 1) * _tmp31 + P(10, 2) * _tmp32 + P(10, 21) * _tmp40 +
P(10, 22) * _tmp41 + P(10, 3) * _tmp35 + P(10, 4) * _tmp38 + P(10, 5) * _tmp39);
_k(11, 0) =
_tmp44 * (P(11, 0) * _tmp31 + P(11, 1) * _tmp33 + P(11, 2) * _tmp34 + P(11, 21) * _tmp42 +
P(11, 22) * _tmp43 + P(11, 3) * _tmp37 + P(11, 4) * _tmp40 + P(11, 5) * _tmp41);
_tmp42 * (P(11, 0) * _tmp30 + P(11, 1) * _tmp31 + P(11, 2) * _tmp32 + P(11, 21) * _tmp40 +
P(11, 22) * _tmp41 + P(11, 3) * _tmp35 + P(11, 4) * _tmp38 + P(11, 5) * _tmp39);
_k(12, 0) =
_tmp44 * (P(12, 0) * _tmp31 + P(12, 1) * _tmp33 + P(12, 2) * _tmp34 + P(12, 21) * _tmp42 +
P(12, 22) * _tmp43 + P(12, 3) * _tmp37 + P(12, 4) * _tmp40 + P(12, 5) * _tmp41);
_tmp42 * (P(12, 0) * _tmp30 + P(12, 1) * _tmp31 + P(12, 2) * _tmp32 + P(12, 21) * _tmp40 +
P(12, 22) * _tmp41 + P(12, 3) * _tmp35 + P(12, 4) * _tmp38 + P(12, 5) * _tmp39);
_k(13, 0) =
_tmp44 * (P(13, 0) * _tmp31 + P(13, 1) * _tmp33 + P(13, 2) * _tmp34 + P(13, 21) * _tmp42 +
P(13, 22) * _tmp43 + P(13, 3) * _tmp37 + P(13, 4) * _tmp40 + P(13, 5) * _tmp41);
_tmp42 * (P(13, 0) * _tmp30 + P(13, 1) * _tmp31 + P(13, 2) * _tmp32 + P(13, 21) * _tmp40 +
P(13, 22) * _tmp41 + P(13, 3) * _tmp35 + P(13, 4) * _tmp38 + P(13, 5) * _tmp39);
_k(14, 0) =
_tmp44 * (P(14, 0) * _tmp31 + P(14, 1) * _tmp33 + P(14, 2) * _tmp34 + P(14, 21) * _tmp42 +
P(14, 22) * _tmp43 + P(14, 3) * _tmp37 + P(14, 4) * _tmp40 + P(14, 5) * _tmp41);
_tmp42 * (P(14, 0) * _tmp30 + P(14, 1) * _tmp31 + P(14, 2) * _tmp32 + P(14, 21) * _tmp40 +
P(14, 22) * _tmp41 + P(14, 3) * _tmp35 + P(14, 4) * _tmp38 + P(14, 5) * _tmp39);
_k(15, 0) =
_tmp44 * (P(15, 0) * _tmp31 + P(15, 1) * _tmp33 + P(15, 2) * _tmp34 + P(15, 21) * _tmp42 +
P(15, 22) * _tmp43 + P(15, 3) * _tmp37 + P(15, 4) * _tmp40 + P(15, 5) * _tmp41);
_tmp42 * (P(15, 0) * _tmp30 + P(15, 1) * _tmp31 + P(15, 2) * _tmp32 + P(15, 21) * _tmp40 +
P(15, 22) * _tmp41 + P(15, 3) * _tmp35 + P(15, 4) * _tmp38 + P(15, 5) * _tmp39);
_k(16, 0) =
_tmp44 * (P(16, 0) * _tmp31 + P(16, 1) * _tmp33 + P(16, 2) * _tmp34 + P(16, 21) * _tmp42 +
P(16, 22) * _tmp43 + P(16, 3) * _tmp37 + P(16, 4) * _tmp40 + P(16, 5) * _tmp41);
_tmp42 * (P(16, 0) * _tmp30 + P(16, 1) * _tmp31 + P(16, 2) * _tmp32 + P(16, 21) * _tmp40 +
P(16, 22) * _tmp41 + P(16, 3) * _tmp35 + P(16, 4) * _tmp38 + P(16, 5) * _tmp39);
_k(17, 0) =
_tmp44 * (P(17, 0) * _tmp31 + P(17, 1) * _tmp33 + P(17, 2) * _tmp34 + P(17, 21) * _tmp42 +
P(17, 22) * _tmp43 + P(17, 3) * _tmp37 + P(17, 4) * _tmp40 + P(17, 5) * _tmp41);
_tmp42 * (P(17, 0) * _tmp30 + P(17, 1) * _tmp31 + P(17, 2) * _tmp32 + P(17, 21) * _tmp40 +
P(17, 22) * _tmp41 + P(17, 3) * _tmp35 + P(17, 4) * _tmp38 + P(17, 5) * _tmp39);
_k(18, 0) =
_tmp44 * (P(18, 0) * _tmp31 + P(18, 1) * _tmp33 + P(18, 2) * _tmp34 + P(18, 21) * _tmp42 +
P(18, 22) * _tmp43 + P(18, 3) * _tmp37 + P(18, 4) * _tmp40 + P(18, 5) * _tmp41);
_tmp42 * (P(18, 0) * _tmp30 + P(18, 1) * _tmp31 + P(18, 2) * _tmp32 + P(18, 21) * _tmp40 +
P(18, 22) * _tmp41 + P(18, 3) * _tmp35 + P(18, 4) * _tmp38 + P(18, 5) * _tmp39);
_k(19, 0) =
_tmp44 * (P(19, 0) * _tmp31 + P(19, 1) * _tmp33 + P(19, 2) * _tmp34 + P(19, 21) * _tmp42 +
P(19, 22) * _tmp43 + P(19, 3) * _tmp37 + P(19, 4) * _tmp40 + P(19, 5) * _tmp41);
_tmp42 * (P(19, 0) * _tmp30 + P(19, 1) * _tmp31 + P(19, 2) * _tmp32 + P(19, 21) * _tmp40 +
P(19, 22) * _tmp41 + P(19, 3) * _tmp35 + P(19, 4) * _tmp38 + P(19, 5) * _tmp39);
_k(20, 0) =
_tmp44 * (P(20, 0) * _tmp31 + P(20, 1) * _tmp33 + P(20, 2) * _tmp34 + P(20, 21) * _tmp42 +
P(20, 22) * _tmp43 + P(20, 3) * _tmp37 + P(20, 4) * _tmp40 + P(20, 5) * _tmp41);
_tmp42 * (P(20, 0) * _tmp30 + P(20, 1) * _tmp31 + P(20, 2) * _tmp32 + P(20, 21) * _tmp40 +
P(20, 22) * _tmp41 + P(20, 3) * _tmp35 + P(20, 4) * _tmp38 + P(20, 5) * _tmp39);
_k(21, 0) =
_tmp44 * (P(21, 0) * _tmp31 + P(21, 1) * _tmp33 + P(21, 2) * _tmp34 + P(21, 21) * _tmp42 +
P(21, 22) * _tmp43 + P(21, 3) * _tmp37 + P(21, 4) * _tmp40 + P(21, 5) * _tmp41);
_tmp42 * (P(21, 0) * _tmp30 + P(21, 1) * _tmp31 + P(21, 2) * _tmp32 + P(21, 21) * _tmp40 +
P(21, 22) * _tmp41 + P(21, 3) * _tmp35 + P(21, 4) * _tmp38 + P(21, 5) * _tmp39);
_k(22, 0) =
_tmp44 * (P(22, 0) * _tmp31 + P(22, 1) * _tmp33 + P(22, 2) * _tmp34 + P(22, 21) * _tmp42 +
P(22, 22) * _tmp43 + P(22, 3) * _tmp37 + P(22, 4) * _tmp40 + P(22, 5) * _tmp41);
_tmp42 * (P(22, 0) * _tmp30 + P(22, 1) * _tmp31 + P(22, 2) * _tmp32 + P(22, 21) * _tmp40 +
P(22, 22) * _tmp41 + P(22, 3) * _tmp35 + P(22, 4) * _tmp38 + P(22, 5) * _tmp39);
_k(23, 0) =
_tmp42 * (P(23, 0) * _tmp30 + P(23, 1) * _tmp31 + P(23, 2) * _tmp32 + P(23, 21) * _tmp40 +
P(23, 22) * _tmp41 + P(23, 3) * _tmp35 + P(23, 4) * _tmp38 + P(23, 5) * _tmp39);
}
} // NOLINT(readability/fn_size)
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_sideslip_innov_and_innov_var.h
+4 -4
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: compute_sideslip_innov_and_innov_var
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
* epsilon: Scalar
*
@@ -26,8 +26,8 @@ namespace sym {
* innov_var: Scalar
*/
template <typename Scalar>
void ComputeSideslipInnovAndInnovVar(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeSideslipInnovAndInnovVar(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
const Scalar epsilon, Scalar* const innov = nullptr,
Scalar* const innov_var = nullptr) {
// Total ops: 265
src/modules/ekf2/EKF/python/ekf_derivation/generated/compute_yaw_innov_var_and_h.h
+7 -7
View File
@@ -16,19 +16,19 @@ namespace sym {
* Symbolic function: compute_yaw_innov_var_and_h
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* R: Scalar
*
* Outputs:
* innov_var: Scalar
* H: Matrix23_1
* H: Matrix24_1
*/
template <typename Scalar>
void ComputeYawInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P, const Scalar R,
void ComputeYawInnovVarAndH(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P, const Scalar R,
Scalar* const innov_var = nullptr,
matrix::Matrix<Scalar, 23, 1>* const H = nullptr) {
matrix::Matrix<Scalar, 24, 1>* const H = nullptr) {
// Total ops: 1
// Unused inputs
@@ -46,7 +46,7 @@ void ComputeYawInnovVarAndH(const matrix::Matrix<Scalar, 24, 1>& state,
}
if (H != nullptr) {
matrix::Matrix<Scalar, 23, 1>& _h = (*H);
matrix::Matrix<Scalar, 24, 1>& _h = (*H);
_h.setZero();
src/modules/ekf2/EKF/python/ekf_derivation/generated/predict_covariance.h
+165 -140
View File
@@ -16,8 +16,8 @@ namespace sym {
* Symbolic function: predict_covariance
*
* Args:
* state: Matrix24_1
* P: Matrix23_23
* state: Matrix25_1
* P: Matrix24_24
* accel: Matrix31
* accel_var: Matrix31
* gyro: Matrix31
@@ -25,23 +25,23 @@ namespace sym {
* dt: Scalar
*
* Outputs:
* res: Matrix23_23
* res: Matrix24_24
*/
template <typename Scalar>
matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24, 1>& state,
const matrix::Matrix<Scalar, 23, 23>& P,
matrix::Matrix<Scalar, 24, 24> PredictCovariance(const matrix::Matrix<Scalar, 25, 1>& state,
const matrix::Matrix<Scalar, 24, 24>& P,
const matrix::Matrix<Scalar, 3, 1>& accel,
const matrix::Matrix<Scalar, 3, 1>& accel_var,
const matrix::Matrix<Scalar, 3, 1>& gyro,
const Scalar gyro_var, const Scalar dt) {
// Total ops: 1754
// Total ops: 1810
// Unused inputs
(void)gyro;
// Input arrays
// Intermediate terms (147)
// Intermediate terms (144)
const Scalar _tmp0 = 2 * state(1, 0);
const Scalar _tmp1 = _tmp0 * state(3, 0);
const Scalar _tmp2 = -_tmp1 * dt;
@@ -177,64 +177,62 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
const Scalar _tmp110 = _tmp109 * dt;
const Scalar _tmp111 = -_tmp1;
const Scalar _tmp112 = _tmp11 + _tmp77;
const Scalar _tmp113 =
_tmp67 * (_tmp111 + _tmp4) + _tmp72 * (_tmp104 + _tmp112) + _tmp74 * (_tmp106 + _tmp78);
const Scalar _tmp114 = _tmp26 * dt;
const Scalar _tmp115 = P(0, 4) * _tmp18 + P(10, 4) * _tmp23 + P(11, 4) * _tmp6 + P(9, 4) * _tmp15;
const Scalar _tmp116 =
const Scalar _tmp113 = dt * (_tmp67 * (_tmp111 + _tmp4) + _tmp72 * (_tmp104 + _tmp112) +
_tmp74 * (_tmp106 + _tmp78));
const Scalar _tmp114 = P(0, 4) * _tmp18 + P(10, 4) * _tmp23 + P(11, 4) * _tmp6 + P(9, 4) * _tmp15;
const Scalar _tmp115 =
P(1, 0) * _tmp18 + P(10, 0) * _tmp29 + P(11, 0) * _tmp34 + P(9, 0) * _tmp36;
const Scalar _tmp117 = _tmp113 * dt;
const Scalar _tmp118 =
const Scalar _tmp116 =
P(1, 4) * _tmp18 + P(10, 4) * _tmp29 + P(11, 4) * _tmp34 + P(9, 4) * _tmp36;
const Scalar _tmp119 =
const Scalar _tmp117 =
P(10, 0) * _tmp45 + P(11, 0) * _tmp44 + P(2, 0) * _tmp18 + P(9, 0) * _tmp47;
const Scalar _tmp120 =
const Scalar _tmp118 =
P(10, 4) * _tmp45 + P(11, 4) * _tmp44 + P(2, 4) * _tmp18 + P(9, 4) * _tmp47;
const Scalar _tmp121 = _tmp56 * _tmp96;
const Scalar _tmp122 = _tmp60 * _tmp97;
const Scalar _tmp123 = P(1, 0) * _tmp75 - P(12, 0) * _tmp57 - P(13, 0) * _tmp61 -
const Scalar _tmp119 = _tmp56 * _tmp96;
const Scalar _tmp120 = _tmp60 * _tmp97;
const Scalar _tmp121 = P(1, 0) * _tmp75 - P(12, 0) * _tmp57 - P(13, 0) * _tmp61 -
P(14, 0) * _tmp64 + P(2, 0) * _tmp79 + P(3, 0);
const Scalar _tmp124 = _tmp63 * _tmp98;
const Scalar _tmp125 = P(1, 4) * _tmp75 - P(12, 4) * _tmp57 - P(13, 4) * _tmp61 -
const Scalar _tmp122 = _tmp63 * _tmp98;
const Scalar _tmp123 = P(1, 4) * _tmp75 - P(12, 4) * _tmp57 - P(13, 4) * _tmp61 -
P(14, 4) * _tmp64 + P(2, 4) * _tmp79 + P(3, 4);
const Scalar _tmp126 = P(0, 0) * _tmp117 - P(12, 0) * _tmp110 - P(13, 0) * _tmp102 -
const Scalar _tmp124 = P(0, 0) * _tmp113 - P(12, 0) * _tmp110 - P(13, 0) * _tmp102 -
P(14, 0) * _tmp108 + P(2, 0) * _tmp105 + P(4, 0);
const Scalar _tmp127 = P(0, 13) * _tmp117 - P(12, 13) * _tmp110 - P(13, 13) * _tmp102 -
const Scalar _tmp125 = P(0, 13) * _tmp113 - P(12, 13) * _tmp110 - P(13, 13) * _tmp102 -
P(14, 13) * _tmp108 + P(2, 13) * _tmp105 + P(4, 13);
const Scalar _tmp128 = P(0, 14) * _tmp117 - P(12, 14) * _tmp110 - P(13, 14) * _tmp102 -
const Scalar _tmp126 = P(0, 14) * _tmp113 - P(12, 14) * _tmp110 - P(13, 14) * _tmp102 -
P(14, 14) * _tmp108 + P(2, 14) * _tmp105 + P(4, 14);
const Scalar _tmp129 = P(0, 12) * _tmp117 - P(12, 12) * _tmp110 - P(13, 12) * _tmp102 -
const Scalar _tmp127 = P(0, 12) * _tmp113 - P(12, 12) * _tmp110 - P(13, 12) * _tmp102 -
P(14, 12) * _tmp108 + P(2, 12) * _tmp105 + P(4, 12);
const Scalar _tmp130 = P(0, 4) * _tmp117 - P(12, 4) * _tmp110 - P(13, 4) * _tmp102 -
const Scalar _tmp128 = P(0, 4) * _tmp113 - P(12, 4) * _tmp110 - P(13, 4) * _tmp102 -
P(14, 4) * _tmp108 + P(2, 4) * _tmp105 + P(4, 4);
const Scalar _tmp131 = _tmp100 + _tmp55;
const Scalar _tmp132 = _tmp131 * dt;
const Scalar _tmp133 =
const Scalar _tmp129 = _tmp100 + _tmp55;
const Scalar _tmp130 = _tmp129 * dt;
const Scalar _tmp131 =
dt * (_tmp67 * (_tmp112 + _tmp69) + _tmp72 * (_tmp111 + _tmp65) + _tmp74 * (_tmp21 + _tmp76));
const Scalar _tmp134 = _tmp73 * dt;
const Scalar _tmp135 = _tmp66 * dt;
const Scalar _tmp136 = _tmp107 * _tmp72 + _tmp109 * _tmp67 + _tmp74 * (_tmp103 + _tmp17 + _tmp68);
const Scalar _tmp137 = P(0, 5) * _tmp18 + P(10, 5) * _tmp23 + P(11, 5) * _tmp6 + P(9, 5) * _tmp15;
const Scalar _tmp138 = _tmp136 * dt;
const Scalar _tmp139 =
const Scalar _tmp132 = _tmp73 * dt;
const Scalar _tmp133 = _tmp66 * dt;
const Scalar _tmp134 =
dt * (_tmp107 * _tmp72 + _tmp109 * _tmp67 + _tmp74 * (_tmp103 + _tmp17 + _tmp68));
const Scalar _tmp135 = P(0, 5) * _tmp18 + P(10, 5) * _tmp23 + P(11, 5) * _tmp6 + P(9, 5) * _tmp15;
const Scalar _tmp136 =
P(1, 5) * _tmp18 + P(10, 5) * _tmp29 + P(11, 5) * _tmp34 + P(9, 5) * _tmp36;
const Scalar _tmp140 =
const Scalar _tmp137 =
P(10, 5) * _tmp45 + P(11, 5) * _tmp44 + P(2, 5) * _tmp18 + P(9, 5) * _tmp47;
const Scalar _tmp141 = P(1, 5) * _tmp75 - P(12, 5) * _tmp57 - P(13, 5) * _tmp61 -
const Scalar _tmp138 = P(1, 5) * _tmp75 - P(12, 5) * _tmp57 - P(13, 5) * _tmp61 -
P(14, 5) * _tmp64 + P(2, 5) * _tmp79 + P(3, 5);
const Scalar _tmp142 = P(0, 5) * _tmp117 - P(12, 5) * _tmp110 - P(13, 5) * _tmp102 -
const Scalar _tmp139 = P(0, 5) * _tmp113 - P(12, 5) * _tmp110 - P(13, 5) * _tmp102 -
P(14, 5) * _tmp108 + P(2, 5) * _tmp105 + P(4, 5);
const Scalar _tmp143 = P(0, 14) * _tmp138 + P(1, 14) * _tmp133 - P(12, 14) * _tmp135 -
P(13, 14) * _tmp134 - P(14, 14) * _tmp132 + P(5, 14);
const Scalar _tmp144 = P(0, 13) * _tmp138 + P(1, 13) * _tmp133 - P(12, 13) * _tmp135 -
P(13, 13) * _tmp134 - P(14, 13) * _tmp132 + P(5, 13);
const Scalar _tmp145 = P(0, 12) * _tmp138 + P(1, 12) * _tmp133 - P(12, 12) * _tmp135 -
P(13, 12) * _tmp134 - P(14, 12) * _tmp132 + P(5, 12);
const Scalar _tmp146 = P(0, 5) * _tmp138 + P(1, 5) * _tmp133 - P(12, 5) * _tmp135 -
P(13, 5) * _tmp134 - P(14, 5) * _tmp132 + P(5, 5);
const Scalar _tmp140 = P(0, 14) * _tmp134 + P(1, 14) * _tmp131 - P(12, 14) * _tmp133 -
P(13, 14) * _tmp132 - P(14, 14) * _tmp130 + P(5, 14);
const Scalar _tmp141 = P(0, 13) * _tmp134 + P(1, 13) * _tmp131 - P(12, 13) * _tmp133 -
P(13, 13) * _tmp132 - P(14, 13) * _tmp130 + P(5, 13);
const Scalar _tmp142 = P(0, 12) * _tmp134 + P(1, 12) * _tmp131 - P(12, 12) * _tmp133 -
P(13, 12) * _tmp132 - P(14, 12) * _tmp130 + P(5, 12);
const Scalar _tmp143 = P(0, 5) * _tmp134 + P(1, 5) * _tmp131 - P(12, 5) * _tmp133 -
P(13, 5) * _tmp132 - P(14, 5) * _tmp130 + P(5, 5);
// Output terms (1)
matrix::Matrix<Scalar, 23, 23> _res;
matrix::Matrix<Scalar, 24, 24> _res;
_res.setZero();
@@ -264,40 +262,40 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
std::pow(_tmp63, Scalar(2)) * _tmp98 - _tmp64 * _tmp94 + _tmp75 * _tmp91 +
_tmp79 * _tmp92 + _tmp99;
_res(0, 4) = -_tmp102 * _tmp58 + _tmp105 * _tmp46 - _tmp108 * _tmp62 - _tmp110 * _tmp53 +
_tmp113 * _tmp114 + _tmp115;
_tmp113 * _tmp26 + _tmp114;
_res(1, 4) = -_tmp102 * _tmp82 + _tmp105 * _tmp48 - _tmp108 * _tmp83 - _tmp110 * _tmp81 +
_tmp116 * _tmp117 + _tmp118;
_tmp113 * _tmp115 + _tmp116;
_res(2, 4) = -_tmp102 * _tmp87 + _tmp105 * _tmp51 - _tmp108 * _tmp88 - _tmp110 * _tmp85 +
_tmp117 * _tmp119 + _tmp120;
_res(3, 4) = _tmp101 * _tmp122 - _tmp102 * _tmp93 + _tmp105 * _tmp92 + _tmp107 * _tmp124 -
_tmp108 * _tmp94 + _tmp109 * _tmp121 - _tmp110 * _tmp90 + _tmp117 * _tmp123 +
_tmp125;
_res(4, 4) = std::pow(_tmp101, Scalar(2)) * _tmp97 - _tmp102 * _tmp127 +
_tmp105 * (P(0, 2) * _tmp117 - P(12, 2) * _tmp110 - P(13, 2) * _tmp102 -
_tmp113 * _tmp117 + _tmp118;
_res(3, 4) = _tmp101 * _tmp120 - _tmp102 * _tmp93 + _tmp105 * _tmp92 + _tmp107 * _tmp122 -
_tmp108 * _tmp94 + _tmp109 * _tmp119 - _tmp110 * _tmp90 + _tmp113 * _tmp121 +
_tmp123;
_res(4, 4) = std::pow(_tmp101, Scalar(2)) * _tmp97 - _tmp102 * _tmp125 +
_tmp105 * (P(0, 2) * _tmp113 - P(12, 2) * _tmp110 - P(13, 2) * _tmp102 -
P(14, 2) * _tmp108 + P(2, 2) * _tmp105 + P(4, 2)) +
std::pow(_tmp107, Scalar(2)) * _tmp98 - _tmp108 * _tmp128 +
std::pow(_tmp109, Scalar(2)) * _tmp96 - _tmp110 * _tmp129 + _tmp117 * _tmp126 +
_tmp130;
_res(0, 5) = _tmp114 * _tmp136 - _tmp132 * _tmp62 + _tmp133 * _tmp35 - _tmp134 * _tmp58 -
_tmp135 * _tmp53 + _tmp137;
_res(1, 5) = _tmp116 * _tmp138 - _tmp132 * _tmp83 + _tmp133 * _tmp43 - _tmp134 * _tmp82 -
_tmp135 * _tmp81 + _tmp139;
_res(2, 5) = _tmp119 * _tmp138 - _tmp132 * _tmp88 + _tmp133 * _tmp86 - _tmp134 * _tmp87 -
_tmp135 * _tmp85 + _tmp140;
_res(3, 5) = _tmp121 * _tmp66 + _tmp122 * _tmp73 + _tmp123 * _tmp138 + _tmp124 * _tmp131 -
_tmp132 * _tmp94 + _tmp133 * _tmp91 - _tmp134 * _tmp93 - _tmp135 * _tmp90 + _tmp141;
_res(4, 5) = _tmp101 * _tmp73 * _tmp97 + _tmp107 * _tmp131 * _tmp98 + _tmp109 * _tmp66 * _tmp96 +
_tmp126 * _tmp138 - _tmp127 * _tmp134 - _tmp128 * _tmp132 - _tmp129 * _tmp135 +
_tmp133 * (P(0, 1) * _tmp117 - P(12, 1) * _tmp110 - P(13, 1) * _tmp102 -
std::pow(_tmp107, Scalar(2)) * _tmp98 - _tmp108 * _tmp126 +
std::pow(_tmp109, Scalar(2)) * _tmp96 - _tmp110 * _tmp127 + _tmp113 * _tmp124 +
_tmp128;
_res(0, 5) = -_tmp130 * _tmp62 + _tmp131 * _tmp35 - _tmp132 * _tmp58 - _tmp133 * _tmp53 +
_tmp134 * _tmp26 + _tmp135;
_res(1, 5) = _tmp115 * _tmp134 - _tmp130 * _tmp83 + _tmp131 * _tmp43 - _tmp132 * _tmp82 -
_tmp133 * _tmp81 + _tmp136;
_res(2, 5) = _tmp117 * _tmp134 - _tmp130 * _tmp88 + _tmp131 * _tmp86 - _tmp132 * _tmp87 -
_tmp133 * _tmp85 + _tmp137;
_res(3, 5) = _tmp119 * _tmp66 + _tmp120 * _tmp73 + _tmp121 * _tmp134 + _tmp122 * _tmp129 -
_tmp130 * _tmp94 + _tmp131 * _tmp91 - _tmp132 * _tmp93 - _tmp133 * _tmp90 + _tmp138;
_res(4, 5) = _tmp101 * _tmp73 * _tmp97 + _tmp107 * _tmp129 * _tmp98 + _tmp109 * _tmp66 * _tmp96 +
_tmp124 * _tmp134 - _tmp125 * _tmp132 - _tmp126 * _tmp130 - _tmp127 * _tmp133 +
_tmp131 * (P(0, 1) * _tmp113 - P(12, 1) * _tmp110 - P(13, 1) * _tmp102 -
P(14, 1) * _tmp108 + P(2, 1) * _tmp105 + P(4, 1)) +
_tmp142;
_res(5, 5) = std::pow(_tmp131, Scalar(2)) * _tmp98 - _tmp132 * _tmp143 +
_tmp133 * (P(0, 1) * _tmp138 + P(1, 1) * _tmp133 - P(12, 1) * _tmp135 -
P(13, 1) * _tmp134 - P(14, 1) * _tmp132 + P(5, 1)) -
_tmp134 * _tmp144 - _tmp135 * _tmp145 +
_tmp138 * (P(0, 0) * _tmp138 + P(1, 0) * _tmp133 - P(12, 0) * _tmp135 -
P(13, 0) * _tmp134 - P(14, 0) * _tmp132 + P(5, 0)) +
_tmp146 + std::pow(_tmp66, Scalar(2)) * _tmp96 +
_tmp139;
_res(5, 5) = std::pow(_tmp129, Scalar(2)) * _tmp98 - _tmp130 * _tmp140 +
_tmp131 * (P(0, 1) * _tmp134 + P(1, 1) * _tmp131 - P(12, 1) * _tmp133 -
P(13, 1) * _tmp132 - P(14, 1) * _tmp130 + P(5, 1)) -
_tmp132 * _tmp141 - _tmp133 * _tmp142 +
_tmp134 * (P(0, 0) * _tmp134 + P(1, 0) * _tmp131 - P(12, 0) * _tmp133 -
P(13, 0) * _tmp132 - P(14, 0) * _tmp130 + P(5, 0)) +
_tmp143 + std::pow(_tmp66, Scalar(2)) * _tmp96 +
std::pow(_tmp73, Scalar(2)) * _tmp97;
_res(0, 6) =
P(0, 6) * _tmp18 + P(10, 6) * _tmp23 + P(11, 6) * _tmp6 + P(9, 6) * _tmp15 + _tmp80 * dt;
@@ -307,43 +305,43 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
P(10, 6) * _tmp45 + P(11, 6) * _tmp44 + P(2, 6) * _tmp18 + P(9, 6) * _tmp47 + _tmp89 * dt;
_res(3, 6) = P(1, 6) * _tmp75 - P(12, 6) * _tmp57 - P(13, 6) * _tmp61 - P(14, 6) * _tmp64 +
P(2, 6) * _tmp79 + P(3, 6) + _tmp99 * dt;
_res(4, 6) = P(0, 6) * _tmp117 - P(12, 6) * _tmp110 - P(13, 6) * _tmp102 - P(14, 6) * _tmp108 +
_res(4, 6) = P(0, 6) * _tmp113 - P(12, 6) * _tmp110 - P(13, 6) * _tmp102 - P(14, 6) * _tmp108 +
P(2, 6) * _tmp105 + P(4, 6) +
dt * (P(0, 3) * _tmp117 - P(12, 3) * _tmp110 - P(13, 3) * _tmp102 -
dt * (P(0, 3) * _tmp113 - P(12, 3) * _tmp110 - P(13, 3) * _tmp102 -
P(14, 3) * _tmp108 + P(2, 3) * _tmp105 + P(4, 3));
_res(5, 6) = P(0, 6) * _tmp138 + P(1, 6) * _tmp133 - P(12, 6) * _tmp135 - P(13, 6) * _tmp134 -
P(14, 6) * _tmp132 + P(5, 6) +
dt * (P(0, 3) * _tmp138 + P(1, 3) * _tmp133 - P(12, 3) * _tmp135 -
P(13, 3) * _tmp134 - P(14, 3) * _tmp132 + P(5, 3));
_res(5, 6) = P(0, 6) * _tmp134 + P(1, 6) * _tmp131 - P(12, 6) * _tmp133 - P(13, 6) * _tmp132 -
P(14, 6) * _tmp130 + P(5, 6) +
dt * (P(0, 3) * _tmp134 + P(1, 3) * _tmp131 - P(12, 3) * _tmp133 -
P(13, 3) * _tmp132 - P(14, 3) * _tmp130 + P(5, 3));
_res(6, 6) = P(3, 6) * dt + P(6, 6) + dt * (P(3, 3) * dt + P(6, 3));
_res(0, 7) =
P(0, 7) * _tmp18 + P(10, 7) * _tmp23 + P(11, 7) * _tmp6 + P(9, 7) * _tmp15 + _tmp115 * dt;
P(0, 7) * _tmp18 + P(10, 7) * _tmp23 + P(11, 7) * _tmp6 + P(9, 7) * _tmp15 + _tmp114 * dt;
_res(1, 7) =
P(1, 7) * _tmp18 + P(10, 7) * _tmp29 + P(11, 7) * _tmp34 + P(9, 7) * _tmp36 + _tmp118 * dt;
P(1, 7) * _tmp18 + P(10, 7) * _tmp29 + P(11, 7) * _tmp34 + P(9, 7) * _tmp36 + _tmp116 * dt;
_res(2, 7) =
P(10, 7) * _tmp45 + P(11, 7) * _tmp44 + P(2, 7) * _tmp18 + P(9, 7) * _tmp47 + _tmp120 * dt;
P(10, 7) * _tmp45 + P(11, 7) * _tmp44 + P(2, 7) * _tmp18 + P(9, 7) * _tmp47 + _tmp118 * dt;
_res(3, 7) = P(1, 7) * _tmp75 - P(12, 7) * _tmp57 - P(13, 7) * _tmp61 - P(14, 7) * _tmp64 +
P(2, 7) * _tmp79 + P(3, 7) + _tmp125 * dt;
_res(4, 7) = P(0, 7) * _tmp117 - P(12, 7) * _tmp110 - P(13, 7) * _tmp102 - P(14, 7) * _tmp108 +
P(2, 7) * _tmp105 + P(4, 7) + _tmp130 * dt;
_res(5, 7) = P(0, 7) * _tmp138 + P(1, 7) * _tmp133 - P(12, 7) * _tmp135 - P(13, 7) * _tmp134 -
P(14, 7) * _tmp132 + P(5, 7) +
dt * (P(0, 4) * _tmp138 + P(1, 4) * _tmp133 - P(12, 4) * _tmp135 -
P(13, 4) * _tmp134 - P(14, 4) * _tmp132 + P(5, 4));
P(2, 7) * _tmp79 + P(3, 7) + _tmp123 * dt;
_res(4, 7) = P(0, 7) * _tmp113 - P(12, 7) * _tmp110 - P(13, 7) * _tmp102 - P(14, 7) * _tmp108 +
P(2, 7) * _tmp105 + P(4, 7) + _tmp128 * dt;
_res(5, 7) = P(0, 7) * _tmp134 + P(1, 7) * _tmp131 - P(12, 7) * _tmp133 - P(13, 7) * _tmp132 -
P(14, 7) * _tmp130 + P(5, 7) +
dt * (P(0, 4) * _tmp134 + P(1, 4) * _tmp131 - P(12, 4) * _tmp133 -
P(13, 4) * _tmp132 - P(14, 4) * _tmp130 + P(5, 4));
_res(6, 7) = P(3, 7) * dt + P(6, 7) + dt * (P(3, 4) * dt + P(6, 4));
_res(7, 7) = P(4, 7) * dt + P(7, 7) + dt * (P(4, 4) * dt + P(7, 4));
_res(0, 8) =
P(0, 8) * _tmp18 + P(10, 8) * _tmp23 + P(11, 8) * _tmp6 + P(9, 8) * _tmp15 + _tmp137 * dt;
P(0, 8) * _tmp18 + P(10, 8) * _tmp23 + P(11, 8) * _tmp6 + P(9, 8) * _tmp15 + _tmp135 * dt;
_res(1, 8) =
P(1, 8) * _tmp18 + P(10, 8) * _tmp29 + P(11, 8) * _tmp34 + P(9, 8) * _tmp36 + _tmp139 * dt;
P(1, 8) * _tmp18 + P(10, 8) * _tmp29 + P(11, 8) * _tmp34 + P(9, 8) * _tmp36 + _tmp136 * dt;
_res(2, 8) =
P(10, 8) * _tmp45 + P(11, 8) * _tmp44 + P(2, 8) * _tmp18 + P(9, 8) * _tmp47 + _tmp140 * dt;
P(10, 8) * _tmp45 + P(11, 8) * _tmp44 + P(2, 8) * _tmp18 + P(9, 8) * _tmp47 + _tmp137 * dt;
_res(3, 8) = P(1, 8) * _tmp75 - P(12, 8) * _tmp57 - P(13, 8) * _tmp61 - P(14, 8) * _tmp64 +
P(2, 8) * _tmp79 + P(3, 8) + _tmp141 * dt;
_res(4, 8) = P(0, 8) * _tmp117 - P(12, 8) * _tmp110 - P(13, 8) * _tmp102 - P(14, 8) * _tmp108 +
P(2, 8) * _tmp105 + P(4, 8) + _tmp142 * dt;
_res(5, 8) = P(0, 8) * _tmp138 + P(1, 8) * _tmp133 - P(12, 8) * _tmp135 - P(13, 8) * _tmp134 -
P(14, 8) * _tmp132 + P(5, 8) + _tmp146 * dt;
P(2, 8) * _tmp79 + P(3, 8) + _tmp138 * dt;
_res(4, 8) = P(0, 8) * _tmp113 - P(12, 8) * _tmp110 - P(13, 8) * _tmp102 - P(14, 8) * _tmp108 +
P(2, 8) * _tmp105 + P(4, 8) + _tmp139 * dt;
_res(5, 8) = P(0, 8) * _tmp134 + P(1, 8) * _tmp131 - P(12, 8) * _tmp133 - P(13, 8) * _tmp132 -
P(14, 8) * _tmp130 + P(5, 8) + _tmp143 * dt;
_res(6, 8) = P(3, 8) * dt + P(6, 8) + dt * (P(3, 5) * dt + P(6, 5));
_res(7, 8) = P(4, 8) * dt + P(7, 8) + dt * (P(4, 5) * dt + P(7, 5));
_res(8, 8) = P(5, 8) * dt + P(8, 8) + dt * (P(5, 5) * dt + P(8, 5));
@@ -352,10 +350,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 9) = _tmp52;
_res(3, 9) = P(1, 9) * _tmp75 - P(12, 9) * _tmp57 - P(13, 9) * _tmp61 - P(14, 9) * _tmp64 +
P(2, 9) * _tmp79 + P(3, 9);
_res(4, 9) = P(0, 9) * _tmp117 - P(12, 9) * _tmp110 - P(13, 9) * _tmp102 - P(14, 9) * _tmp108 +
_res(4, 9) = P(0, 9) * _tmp113 - P(12, 9) * _tmp110 - P(13, 9) * _tmp102 - P(14, 9) * _tmp108 +
P(2, 9) * _tmp105 + P(4, 9);
_res(5, 9) = P(0, 9) * _tmp138 + P(1, 9) * _tmp133 - P(12, 9) * _tmp135 - P(13, 9) * _tmp134 -
P(14, 9) * _tmp132 + P(5, 9);
_res(5, 9) = P(0, 9) * _tmp134 + P(1, 9) * _tmp131 - P(12, 9) * _tmp133 - P(13, 9) * _tmp132 -
P(14, 9) * _tmp130 + P(5, 9);
_res(6, 9) = P(3, 9) * dt + P(6, 9);
_res(7, 9) = P(4, 9) * dt + P(7, 9);
_res(8, 9) = P(5, 9) * dt + P(8, 9);
@@ -365,10 +363,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 10) = _tmp49;
_res(3, 10) = P(1, 10) * _tmp75 - P(12, 10) * _tmp57 - P(13, 10) * _tmp61 - P(14, 10) * _tmp64 +
P(2, 10) * _tmp79 + P(3, 10);
_res(4, 10) = P(0, 10) * _tmp117 - P(12, 10) * _tmp110 - P(13, 10) * _tmp102 -
_res(4, 10) = P(0, 10) * _tmp113 - P(12, 10) * _tmp110 - P(13, 10) * _tmp102 -
P(14, 10) * _tmp108 + P(2, 10) * _tmp105 + P(4, 10);
_res(5, 10) = P(0, 10) * _tmp138 + P(1, 10) * _tmp133 - P(12, 10) * _tmp135 -
P(13, 10) * _tmp134 - P(14, 10) * _tmp132 + P(5, 10);
_res(5, 10) = P(0, 10) * _tmp134 + P(1, 10) * _tmp131 - P(12, 10) * _tmp133 -
P(13, 10) * _tmp132 - P(14, 10) * _tmp130 + P(5, 10);
_res(6, 10) = P(3, 10) * dt + P(6, 10);
_res(7, 10) = P(4, 10) * dt + P(7, 10);
_res(8, 10) = P(5, 10) * dt + P(8, 10);
@@ -379,10 +377,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 11) = _tmp50;
_res(3, 11) = P(1, 11) * _tmp75 - P(12, 11) * _tmp57 - P(13, 11) * _tmp61 - P(14, 11) * _tmp64 +
P(2, 11) * _tmp79 + P(3, 11);
_res(4, 11) = P(0, 11) * _tmp117 - P(12, 11) * _tmp110 - P(13, 11) * _tmp102 -
_res(4, 11) = P(0, 11) * _tmp113 - P(12, 11) * _tmp110 - P(13, 11) * _tmp102 -
P(14, 11) * _tmp108 + P(2, 11) * _tmp105 + P(4, 11);
_res(5, 11) = P(0, 11) * _tmp138 + P(1, 11) * _tmp133 - P(12, 11) * _tmp135 -
P(13, 11) * _tmp134 - P(14, 11) * _tmp132 + P(5, 11);
_res(5, 11) = P(0, 11) * _tmp134 + P(1, 11) * _tmp131 - P(12, 11) * _tmp133 -
P(13, 11) * _tmp132 - P(14, 11) * _tmp130 + P(5, 11);
_res(6, 11) = P(3, 11) * dt + P(6, 11);
_res(7, 11) = P(4, 11) * dt + P(7, 11);
_res(8, 11) = P(5, 11) * dt + P(8, 11);
@@ -393,8 +391,8 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(1, 12) = _tmp81;
_res(2, 12) = _tmp85;
_res(3, 12) = _tmp90;
_res(4, 12) = _tmp129;
_res(5, 12) = _tmp145;
_res(4, 12) = _tmp127;
_res(5, 12) = _tmp142;
_res(6, 12) = P(3, 12) * dt + P(6, 12);
_res(7, 12) = P(4, 12) * dt + P(7, 12);
_res(8, 12) = P(5, 12) * dt + P(8, 12);
@@ -406,8 +404,8 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(1, 13) = _tmp82;
_res(2, 13) = _tmp87;
_res(3, 13) = _tmp93;
_res(4, 13) = _tmp127;
_res(5, 13) = _tmp144;
_res(4, 13) = _tmp125;
_res(5, 13) = _tmp141;
_res(6, 13) = P(3, 13) * dt + P(6, 13);
_res(7, 13) = P(4, 13) * dt + P(7, 13);
_res(8, 13) = P(5, 13) * dt + P(8, 13);
@@ -420,8 +418,8 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(1, 14) = _tmp83;
_res(2, 14) = _tmp88;
_res(3, 14) = _tmp94;
_res(4, 14) = _tmp128;
_res(5, 14) = _tmp143;
_res(4, 14) = _tmp126;
_res(5, 14) = _tmp140;
_res(6, 14) = P(3, 14) * dt + P(6, 14);
_res(7, 14) = P(4, 14) * dt + P(7, 14);
_res(8, 14) = P(5, 14) * dt + P(8, 14);
@@ -436,10 +434,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 15) = P(10, 15) * _tmp45 + P(11, 15) * _tmp44 + P(2, 15) * _tmp18 + P(9, 15) * _tmp47;
_res(3, 15) = P(1, 15) * _tmp75 - P(12, 15) * _tmp57 - P(13, 15) * _tmp61 - P(14, 15) * _tmp64 +
P(2, 15) * _tmp79 + P(3, 15);
_res(4, 15) = P(0, 15) * _tmp117 - P(12, 15) * _tmp110 - P(13, 15) * _tmp102 -
_res(4, 15) = P(0, 15) * _tmp113 - P(12, 15) * _tmp110 - P(13, 15) * _tmp102 -
P(14, 15) * _tmp108 + P(2, 15) * _tmp105 + P(4, 15);
_res(5, 15) = P(0, 15) * _tmp138 + P(1, 15) * _tmp133 - P(12, 15) * _tmp135 -
P(13, 15) * _tmp134 - P(14, 15) * _tmp132 + P(5, 15);
_res(5, 15) = P(0, 15) * _tmp134 + P(1, 15) * _tmp131 - P(12, 15) * _tmp133 -
P(13, 15) * _tmp132 - P(14, 15) * _tmp130 + P(5, 15);
_res(6, 15) = P(3, 15) * dt + P(6, 15);
_res(7, 15) = P(4, 15) * dt + P(7, 15);
_res(8, 15) = P(5, 15) * dt + P(8, 15);
@@ -455,10 +453,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 16) = P(10, 16) * _tmp45 + P(11, 16) * _tmp44 + P(2, 16) * _tmp18 + P(9, 16) * _tmp47;
_res(3, 16) = P(1, 16) * _tmp75 - P(12, 16) * _tmp57 - P(13, 16) * _tmp61 - P(14, 16) * _tmp64 +
P(2, 16) * _tmp79 + P(3, 16);
_res(4, 16) = P(0, 16) * _tmp117 - P(12, 16) * _tmp110 - P(13, 16) * _tmp102 -
_res(4, 16) = P(0, 16) * _tmp113 - P(12, 16) * _tmp110 - P(13, 16) * _tmp102 -
P(14, 16) * _tmp108 + P(2, 16) * _tmp105 + P(4, 16);
_res(5, 16) = P(0, 16) * _tmp138 + P(1, 16) * _tmp133 - P(12, 16) * _tmp135 -
P(13, 16) * _tmp134 - P(14, 16) * _tmp132 + P(5, 16);
_res(5, 16) = P(0, 16) * _tmp134 + P(1, 16) * _tmp131 - P(12, 16) * _tmp133 -
P(13, 16) * _tmp132 - P(14, 16) * _tmp130 + P(5, 16);
_res(6, 16) = P(3, 16) * dt + P(6, 16);
_res(7, 16) = P(4, 16) * dt + P(7, 16);
_res(8, 16) = P(5, 16) * dt + P(8, 16);
@@ -475,10 +473,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 17) = P(10, 17) * _tmp45 + P(11, 17) * _tmp44 + P(2, 17) * _tmp18 + P(9, 17) * _tmp47;
_res(3, 17) = P(1, 17) * _tmp75 - P(12, 17) * _tmp57 - P(13, 17) * _tmp61 - P(14, 17) * _tmp64 +
P(2, 17) * _tmp79 + P(3, 17);
_res(4, 17) = P(0, 17) * _tmp117 - P(12, 17) * _tmp110 - P(13, 17) * _tmp102 -
_res(4, 17) = P(0, 17) * _tmp113 - P(12, 17) * _tmp110 - P(13, 17) * _tmp102 -
P(14, 17) * _tmp108 + P(2, 17) * _tmp105 + P(4, 17);
_res(5, 17) = P(0, 17) * _tmp138 + P(1, 17) * _tmp133 - P(12, 17) * _tmp135 -
P(13, 17) * _tmp134 - P(14, 17) * _tmp132 + P(5, 17);
_res(5, 17) = P(0, 17) * _tmp134 + P(1, 17) * _tmp131 - P(12, 17) * _tmp133 -
P(13, 17) * _tmp132 - P(14, 17) * _tmp130 + P(5, 17);
_res(6, 17) = P(3, 17) * dt + P(6, 17);
_res(7, 17) = P(4, 17) * dt + P(7, 17);
_res(8, 17) = P(5, 17) * dt + P(8, 17);
@@ -496,10 +494,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 18) = P(10, 18) * _tmp45 + P(11, 18) * _tmp44 + P(2, 18) * _tmp18 + P(9, 18) * _tmp47;
_res(3, 18) = P(1, 18) * _tmp75 - P(12, 18) * _tmp57 - P(13, 18) * _tmp61 - P(14, 18) * _tmp64 +
P(2, 18) * _tmp79 + P(3, 18);
_res(4, 18) = P(0, 18) * _tmp117 - P(12, 18) * _tmp110 - P(13, 18) * _tmp102 -
_res(4, 18) = P(0, 18) * _tmp113 - P(12, 18) * _tmp110 - P(13, 18) * _tmp102 -
P(14, 18) * _tmp108 + P(2, 18) * _tmp105 + P(4, 18);
_res(5, 18) = P(0, 18) * _tmp138 + P(1, 18) * _tmp133 - P(12, 18) * _tmp135 -
P(13, 18) * _tmp134 - P(14, 18) * _tmp132 + P(5, 18);
_res(5, 18) = P(0, 18) * _tmp134 + P(1, 18) * _tmp131 - P(12, 18) * _tmp133 -
P(13, 18) * _tmp132 - P(14, 18) * _tmp130 + P(5, 18);
_res(6, 18) = P(3, 18) * dt + P(6, 18);
_res(7, 18) = P(4, 18) * dt + P(7, 18);
_res(8, 18) = P(5, 18) * dt + P(8, 18);
@@ -518,10 +516,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 19) = P(10, 19) * _tmp45 + P(11, 19) * _tmp44 + P(2, 19) * _tmp18 + P(9, 19) * _tmp47;
_res(3, 19) = P(1, 19) * _tmp75 - P(12, 19) * _tmp57 - P(13, 19) * _tmp61 - P(14, 19) * _tmp64 +
P(2, 19) * _tmp79 + P(3, 19);
_res(4, 19) = P(0, 19) * _tmp117 - P(12, 19) * _tmp110 - P(13, 19) * _tmp102 -
_res(4, 19) = P(0, 19) * _tmp113 - P(12, 19) * _tmp110 - P(13, 19) * _tmp102 -
P(14, 19) * _tmp108 + P(2, 19) * _tmp105 + P(4, 19);
_res(5, 19) = P(0, 19) * _tmp138 + P(1, 19) * _tmp133 - P(12, 19) * _tmp135 -
P(13, 19) * _tmp134 - P(14, 19) * _tmp132 + P(5, 19);
_res(5, 19) = P(0, 19) * _tmp134 + P(1, 19) * _tmp131 - P(12, 19) * _tmp133 -
P(13, 19) * _tmp132 - P(14, 19) * _tmp130 + P(5, 19);
_res(6, 19) = P(3, 19) * dt + P(6, 19);
_res(7, 19) = P(4, 19) * dt + P(7, 19);
_res(8, 19) = P(5, 19) * dt + P(8, 19);
@@ -541,10 +539,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 20) = P(10, 20) * _tmp45 + P(11, 20) * _tmp44 + P(2, 20) * _tmp18 + P(9, 20) * _tmp47;
_res(3, 20) = P(1, 20) * _tmp75 - P(12, 20) * _tmp57 - P(13, 20) * _tmp61 - P(14, 20) * _tmp64 +
P(2, 20) * _tmp79 + P(3, 20);
_res(4, 20) = P(0, 20) * _tmp117 - P(12, 20) * _tmp110 - P(13, 20) * _tmp102 -
_res(4, 20) = P(0, 20) * _tmp113 - P(12, 20) * _tmp110 - P(13, 20) * _tmp102 -
P(14, 20) * _tmp108 + P(2, 20) * _tmp105 + P(4, 20);
_res(5, 20) = P(0, 20) * _tmp138 + P(1, 20) * _tmp133 - P(12, 20) * _tmp135 -
P(13, 20) * _tmp134 - P(14, 20) * _tmp132 + P(5, 20);
_res(5, 20) = P(0, 20) * _tmp134 + P(1, 20) * _tmp131 - P(12, 20) * _tmp133 -
P(13, 20) * _tmp132 - P(14, 20) * _tmp130 + P(5, 20);
_res(6, 20) = P(3, 20) * dt + P(6, 20);
_res(7, 20) = P(4, 20) * dt + P(7, 20);
_res(8, 20) = P(5, 20) * dt + P(8, 20);
@@ -565,10 +563,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 21) = P(10, 21) * _tmp45 + P(11, 21) * _tmp44 + P(2, 21) * _tmp18 + P(9, 21) * _tmp47;
_res(3, 21) = P(1, 21) * _tmp75 - P(12, 21) * _tmp57 - P(13, 21) * _tmp61 - P(14, 21) * _tmp64 +
P(2, 21) * _tmp79 + P(3, 21);
_res(4, 21) = P(0, 21) * _tmp117 - P(12, 21) * _tmp110 - P(13, 21) * _tmp102 -
_res(4, 21) = P(0, 21) * _tmp113 - P(12, 21) * _tmp110 - P(13, 21) * _tmp102 -
P(14, 21) * _tmp108 + P(2, 21) * _tmp105 + P(4, 21);
_res(5, 21) = P(0, 21) * _tmp138 + P(1, 21) * _tmp133 - P(12, 21) * _tmp135 -
P(13, 21) * _tmp134 - P(14, 21) * _tmp132 + P(5, 21);
_res(5, 21) = P(0, 21) * _tmp134 + P(1, 21) * _tmp131 - P(12, 21) * _tmp133 -
P(13, 21) * _tmp132 - P(14, 21) * _tmp130 + P(5, 21);
_res(6, 21) = P(3, 21) * dt + P(6, 21);
_res(7, 21) = P(4, 21) * dt + P(7, 21);
_res(8, 21) = P(5, 21) * dt + P(8, 21);
@@ -590,10 +588,10 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(2, 22) = P(10, 22) * _tmp45 + P(11, 22) * _tmp44 + P(2, 22) * _tmp18 + P(9, 22) * _tmp47;
_res(3, 22) = P(1, 22) * _tmp75 - P(12, 22) * _tmp57 - P(13, 22) * _tmp61 - P(14, 22) * _tmp64 +
P(2, 22) * _tmp79 + P(3, 22);
_res(4, 22) = P(0, 22) * _tmp117 - P(12, 22) * _tmp110 - P(13, 22) * _tmp102 -
_res(4, 22) = P(0, 22) * _tmp113 - P(12, 22) * _tmp110 - P(13, 22) * _tmp102 -
P(14, 22) * _tmp108 + P(2, 22) * _tmp105 + P(4, 22);
_res(5, 22) = P(0, 22) * _tmp138 + P(1, 22) * _tmp133 - P(12, 22) * _tmp135 -
P(13, 22) * _tmp134 - P(14, 22) * _tmp132 + P(5, 22);
_res(5, 22) = P(0, 22) * _tmp134 + P(1, 22) * _tmp131 - P(12, 22) * _tmp133 -
P(13, 22) * _tmp132 - P(14, 22) * _tmp130 + P(5, 22);
_res(6, 22) = P(3, 22) * dt + P(6, 22);
_res(7, 22) = P(4, 22) * dt + P(7, 22);
_res(8, 22) = P(5, 22) * dt + P(8, 22);
@@ -611,6 +609,33 @@ matrix::Matrix<Scalar, 23, 23> PredictCovariance(const matrix::Matrix<Scalar, 24
_res(20, 22) = P(20, 22);
_res(21, 22) = P(21, 22);
_res(22, 22) = P(22, 22);
_res(0, 23) = P(0, 23) * _tmp18 + P(10, 23) * _tmp23 + P(11, 23) * _tmp6 + P(9, 23) * _tmp15;
_res(1, 23) = P(1, 23) * _tmp18 + P(10, 23) * _tmp29 + P(11, 23) * _tmp34 + P(9, 23) * _tmp36;
_res(2, 23) = P(10, 23) * _tmp45 + P(11, 23) * _tmp44 + P(2, 23) * _tmp18 + P(9, 23) * _tmp47;
_res(3, 23) = P(1, 23) * _tmp75 - P(12, 23) * _tmp57 - P(13, 23) * _tmp61 - P(14, 23) * _tmp64 +
P(2, 23) * _tmp79 + P(3, 23);
_res(4, 23) = P(0, 23) * _tmp113 - P(12, 23) * _tmp110 - P(13, 23) * _tmp102 -
P(14, 23) * _tmp108 + P(2, 23) * _tmp105 + P(4, 23);
_res(5, 23) = P(0, 23) * _tmp134 + P(1, 23) * _tmp131 - P(12, 23) * _tmp133 -
P(13, 23) * _tmp132 - P(14, 23) * _tmp130 + P(5, 23);
_res(6, 23) = P(3, 23) * dt + P(6, 23);
_res(7, 23) = P(4, 23) * dt + P(7, 23);
_res(8, 23) = P(5, 23) * dt + P(8, 23);
_res(9, 23) = P(9, 23);
_res(10, 23) = P(10, 23);
_res(11, 23) = P(11, 23);
_res(12, 23) = P(12, 23);
_res(13, 23) = P(13, 23);
_res(14, 23) = P(14, 23);
_res(15, 23) = P(15, 23);
_res(16, 23) = P(16, 23);
_res(17, 23) = P(17, 23);
_res(18, 23) = P(18, 23);
_res(19, 23) = P(19, 23);
_res(20, 23) = P(20, 23);
_res(21, 23) = P(21, 23);
_res(22, 23) = P(22, 23);
_res(23, 23) = P(23, 23);
return _res;
} // NOLINT(readability/fn_size)
src/modules/ekf2/EKF/python/ekf_derivation/generated/state.h
+9 -6
View File
@@ -18,9 +18,10 @@ struct StateSample {
matrix::Vector3<float> mag_I{};
matrix::Vector3<float> mag_B{};
matrix::Vector2<float> wind_vel{};
float terrain{};
matrix::Vector<float, 24> Data() const {
matrix::Vector<float, 24> state;
matrix::Vector<float, 25> Data() const {
matrix::Vector<float, 25> state;
state.slice<4, 1>(0, 0) = quat_nominal;
state.slice<3, 1>(4, 0) = vel;
state.slice<3, 1>(7, 0) = pos;
@@ -29,15 +30,16 @@ struct StateSample {
state.slice<3, 1>(16, 0) = mag_I;
state.slice<3, 1>(19, 0) = mag_B;
state.slice<2, 1>(22, 0) = wind_vel;
state.slice<1, 1>(24, 0) = terrain;
return state;
};
const matrix::Vector<float, 24>& vector() const {
return *reinterpret_cast<matrix::Vector<float, 24>*>(const_cast<float*>(reinterpret_cast<const float*>(&quat_nominal)));
const matrix::Vector<float, 25>& vector() const {
return *reinterpret_cast<matrix::Vector<float, 25>*>(const_cast<float*>(reinterpret_cast<const float*>(&quat_nominal)));
};
};
static_assert(sizeof(matrix::Vector<float, 24>) == sizeof(StateSample), "state vector doesn't match StateSample size");
static_assert(sizeof(matrix::Vector<float, 25>) == sizeof(StateSample), "state vector doesn't match StateSample size");
struct IdxDof { unsigned idx; unsigned dof; };
namespace State {
@@ -49,7 +51,8 @@ namespace State {
static constexpr IdxDof mag_I{15, 3};
static constexpr IdxDof mag_B{18, 3};
static constexpr IdxDof wind_vel{21, 2};
static constexpr uint8_t size{23};
static constexpr IdxDof terrain{23, 1};
static constexpr uint8_t size{24};
};
}
#endif // !EKF_STATE_H
src/modules/ekf2/EKF/terrain_estimator/terrain_estimator.cpp
+10 -351
View File
@@ -38,26 +38,17 @@
*/
#include "ekf.h"
#include "terrain_estimator/derivation/generated/terr_est_compute_flow_xy_innov_var_and_hx.h"
#include "terrain_estimator/derivation/generated/terr_est_compute_flow_y_innov_var_and_h.h"
#include "ekf_derivation/generated/state.h"
#include <mathlib/mathlib.h>
void Ekf::initHagl()
{
// assume a ground clearance
_terrain_vpos = _state.pos(2) + _params.rng_gnd_clearance;
_state.terrain = _state.pos(2) + _params.rng_gnd_clearance;
// use the ground clearance value as our uncertainty
_terrain_var = sq(_params.rng_gnd_clearance);
#if defined(CONFIG_EKF2_RANGE_FINDER)
_aid_src_terrain_range_finder.time_last_fuse = _time_delayed_us;
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
_aid_src_terrain_optical_flow.time_last_fuse = _time_delayed_us;
#endif // CONFIG_EKF2_OPTICAL_FLOW
P.uncorrelateCovarianceSetVariance<State::terrain.dof>(State::terrain.idx, sq(_params.rng_gnd_clearance));
}
void Ekf::runTerrainEstimator(const imuSample &imu_delayed)
@@ -74,333 +65,9 @@ void Ekf::runTerrainEstimator(const imuSample &imu_delayed)
initHagl();
}
predictHagl(imu_delayed);
#if defined(CONFIG_EKF2_RANGE_FINDER)
controlHaglRngFusion();
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
controlHaglFlowFusion();
#endif // CONFIG_EKF2_OPTICAL_FLOW
controlHaglFakeFusion();
// constrain _terrain_vpos to be a minimum of _params.rng_gnd_clearance larger than _state.pos(2)
if (_terrain_vpos - _state.pos(2) < _params.rng_gnd_clearance) {
_terrain_vpos = _params.rng_gnd_clearance + _state.pos(2);
}
}
void Ekf::predictHagl(const imuSample &imu_delayed)
{
// predict the state variance growth where the state is the vertical position of the terrain underneath the vehicle
// process noise due to errors in vehicle height estimate
_terrain_var += sq(imu_delayed.delta_vel_dt * _params.terrain_p_noise);
// process noise due to terrain gradient
_terrain_var += sq(imu_delayed.delta_vel_dt * _params.terrain_gradient)
* (sq(_state.vel(0)) + sq(_state.vel(1)));
// limit the variance to prevent it becoming badly conditioned
_terrain_var = math::constrain(_terrain_var, 0.0f, 1e4f);
}
#if defined(CONFIG_EKF2_RANGE_FINDER)
void Ekf::controlHaglRngFusion()
{
if (!(_params.terrain_fusion_mode & TerrainFusionMask::TerrainFuseRangeFinder)
|| _control_status.flags.rng_fault) {
stopHaglRngFusion();
return;
}
if (_range_sensor.isDataReady()) {
updateHaglRng(_aid_src_terrain_range_finder);
}
if (_range_sensor.isDataHealthy()) {
const bool continuing_conditions_passing = _rng_consistency_check.isKinematicallyConsistent();
//&& !_control_status.flags.rng_hgt // TODO: should not be fused when using range height
const bool starting_conditions_passing = continuing_conditions_passing
&& _range_sensor.isRegularlySendingData()
&& (_rng_consistency_check.getTestRatio() < 1.f);
_time_last_healthy_rng_data = _time_delayed_us;
if (_hagl_sensor_status.flags.range_finder) {
if (continuing_conditions_passing) {
fuseHaglRng(_aid_src_terrain_range_finder);
// We have been rejecting range data for too long
const uint64_t timeout = static_cast<uint64_t>(_params.terrain_timeout * 1e6f);
const bool is_fusion_failing = isTimedOut(_aid_src_terrain_range_finder.time_last_fuse, timeout);
if (is_fusion_failing) {
if (_range_sensor.getDistBottom() > 2.f * _params.rng_gnd_clearance) {
// Data seems good, attempt a reset
resetHaglRng();
} else if (starting_conditions_passing) {
// The sensor can probably not detect the ground properly
// declare the sensor faulty and stop the fusion
_control_status.flags.rng_fault = true;
_range_sensor.setFaulty(true);
stopHaglRngFusion();
} else {
// This could be a temporary issue, stop the fusion without declaring the sensor faulty
stopHaglRngFusion();
}
}
} else {
stopHaglRngFusion();
}
} else {
if (starting_conditions_passing) {
_hagl_sensor_status.flags.range_finder = true;
// Reset the state to the measurement only if the test ratio is large,
// otherwise let it converge through the fusion
if (_hagl_sensor_status.flags.flow && (_aid_src_terrain_range_finder.test_ratio < 0.2f)) {
fuseHaglRng(_aid_src_terrain_range_finder);
} else {
resetHaglRng();
}
}
}
} else if (_hagl_sensor_status.flags.range_finder && isTimedOut(_time_last_healthy_rng_data, _params.reset_timeout_max)) {
// No data anymore. Stop until it comes back.
stopHaglRngFusion();
}
}
float Ekf::getRngVar() const
{
return fmaxf(P(State::pos.idx + 2, State::pos.idx + 2) * _params.vehicle_variance_scaler, 0.0f)
+ sq(_params.range_noise)
+ sq(_params.range_noise_scaler * _range_sensor.getRange());
}
void Ekf::resetHaglRng()
{
_terrain_vpos = _state.pos(2) + _range_sensor.getDistBottom();
_terrain_var = getRngVar();
_terrain_vpos_reset_counter++;
_aid_src_terrain_range_finder.time_last_fuse = _time_delayed_us;
}
void Ekf::stopHaglRngFusion()
{
if (_hagl_sensor_status.flags.range_finder) {
ECL_INFO("stopping HAGL range fusion");
// reset flags
_innov_check_fail_status.flags.reject_hagl = false;
_hagl_sensor_status.flags.range_finder = false;
}
}
void Ekf::updateHaglRng(estimator_aid_source1d_s &aid_src) const
{
// get a height above ground measurement from the range finder assuming a flat earth
const float meas_hagl = _range_sensor.getDistBottom();
// predict the hagl from the vehicle position and terrain height
const float pred_hagl = _terrain_vpos - _state.pos(2);
// calculate the observation variance adding the variance of the vehicles own height uncertainty
const float obs_variance = getRngVar();
// calculate the innovation variance - limiting it to prevent a badly conditioned fusion
const float hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance);
updateAidSourceStatus(aid_src,
_time_delayed_us, // sample timestamp
meas_hagl, // observation
obs_variance, // observation variance
pred_hagl - meas_hagl, // innovation
hagl_innov_var, // innovation variance
math::max(_params.range_innov_gate, 1.f)); // innovation gate
}
void Ekf::fuseHaglRng(estimator_aid_source1d_s &aid_src)
{
if (!aid_src.innovation_rejected) {
// calculate the Kalman gain
const float gain = _terrain_var / aid_src.innovation_variance;
// correct the state
_terrain_vpos -= gain * aid_src.innovation;
// correct the variance
_terrain_var = fmaxf(_terrain_var * (1.0f - gain), 0.0f);
// record last successful fusion event
_innov_check_fail_status.flags.reject_hagl = false;
aid_src.time_last_fuse = _time_delayed_us;
aid_src.fused = true;
} else {
_innov_check_fail_status.flags.reject_hagl = true;
aid_src.fused = false;
}
}
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
void Ekf::controlHaglFlowFusion()
{
if (!(_params.terrain_fusion_mode & TerrainFusionMask::TerrainFuseOpticalFlow)) {
stopHaglFlowFusion();
return;
}
if (_flow_data_ready) {
updateOptFlow(_aid_src_terrain_optical_flow);
const bool continuing_conditions_passing = _control_status.flags.in_air
&& !_control_status.flags.opt_flow
&& _control_status.flags.gps
&& !_hagl_sensor_status.flags.range_finder;
const bool starting_conditions_passing = continuing_conditions_passing;
if (_hagl_sensor_status.flags.flow) {
if (continuing_conditions_passing) {
// TODO: wait until the midpoint of the flow sample has fallen behind the fusion time horizon
fuseFlowForTerrain(_aid_src_terrain_optical_flow);
// TODO: do something when failing continuously the innovation check
/* const bool is_fusion_failing = isTimedOut(_aid_src_optical_flow.time_last_fuse, _params.reset_timeout_max); */
/* if (is_fusion_failing) { */
/* resetHaglFlow(); */
/* } */
} else {
stopHaglFlowFusion();
}
} else {
if (starting_conditions_passing) {
_hagl_sensor_status.flags.flow = true;
// TODO: do a reset instead of trying to fuse the data?
fuseFlowForTerrain(_aid_src_terrain_optical_flow);
}
}
_flow_data_ready = false;
} else if (_hagl_sensor_status.flags.flow && (_time_delayed_us > _flow_sample_delayed.time_us + (uint64_t)5e6)) {
// No data anymore. Stop until it comes back.
stopHaglFlowFusion();
}
}
void Ekf::stopHaglFlowFusion()
{
if (_hagl_sensor_status.flags.flow) {
ECL_INFO("stopping HAGL flow fusion");
_hagl_sensor_status.flags.flow = false;
}
}
void Ekf::resetHaglFlow()
{
// TODO: use the flow data
_terrain_vpos = fmaxf(0.0f, _state.pos(2));
_terrain_var = 100.0f;
_terrain_vpos_reset_counter++;
_aid_src_terrain_optical_flow.time_last_fuse = _time_delayed_us;
}
void Ekf::fuseFlowForTerrain(estimator_aid_source2d_s &flow)
{
flow.fused = true;
const float R_LOS = flow.observation_variance[0];
// calculate the height above the ground of the optical flow camera. Since earth frame is NED
// a positive offset in earth frame leads to a smaller height above the ground.
float range = predictFlowRange();
const float state = _terrain_vpos; // linearize both axes using the same state value
Vector2f innov_var;
float H;
sym::TerrEstComputeFlowXyInnovVarAndHx(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &innov_var, &H);
innov_var.copyTo(flow.innovation_variance);
if ((flow.innovation_variance[0] < R_LOS)
|| (flow.innovation_variance[1] < R_LOS)) {
// we need to reinitialise the covariance matrix and abort this fusion step
ECL_ERR("Opt flow error - covariance reset");
_terrain_var = 100.0f;
return;
}
_innov_check_fail_status.flags.reject_optflow_X = (flow.test_ratio[0] > 1.f);
_innov_check_fail_status.flags.reject_optflow_Y = (flow.test_ratio[1] > 1.f);
// if either axis fails we abort the fusion
if (flow.innovation_rejected) {
return;
}
// fuse observation axes sequentially
for (uint8_t index = 0; index <= 1; index++) {
if (index == 0) {
// everything was already computed above
} else if (index == 1) {
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
sym::TerrEstComputeFlowYInnovVarAndH(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &flow.innovation_variance[1], &H);
// recalculate the innovation using the updated state
const Vector2f vel_body = predictFlowVelBody();
range = predictFlowRange();
flow.innovation[1] = (-vel_body(0) / range) - flow.observation[1];
if (flow.innovation_variance[1] < R_LOS) {
// we need to reinitialise the covariance matrix and abort this fusion step
ECL_ERR("Opt flow error - covariance reset");
_terrain_var = 100.0f;
return;
}
}
float Kfusion = _terrain_var * H / flow.innovation_variance[index];
_terrain_vpos += Kfusion * flow.innovation[0];
// constrain terrain to minimum allowed value and predict height above ground
_terrain_vpos = fmaxf(_terrain_vpos, _params.rng_gnd_clearance + _state.pos(2));
// guard against negative variance
_terrain_var = fmaxf(_terrain_var - Kfusion * H * _terrain_var, sq(0.01f));
}
_fault_status.flags.bad_optflow_X = false;
_fault_status.flags.bad_optflow_Y = false;
flow.time_last_fuse = _time_delayed_us;
flow.fused = true;
}
#endif // CONFIG_EKF2_OPTICAL_FLOW
void Ekf::controlHaglFakeFusion()
{
if (!_control_status.flags.in_air
@@ -410,11 +77,11 @@ void Ekf::controlHaglFakeFusion()
bool recent_terrain_aiding = false;
#if defined(CONFIG_EKF2_RANGE_FINDER)
recent_terrain_aiding |= isRecent(_aid_src_terrain_range_finder.time_last_fuse, (uint64_t)1e6);
recent_terrain_aiding |= isRecent(_aid_src_rng_hgt.time_last_fuse, (uint64_t)1e6);
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
recent_terrain_aiding |= isRecent(_aid_src_terrain_optical_flow.time_last_fuse, (uint64_t)1e6);
recent_terrain_aiding |= isRecent(_aid_src_optical_flow.time_last_fuse, (uint64_t)1e6);
#endif // CONFIG_EKF2_OPTICAL_FLOW
if (_control_status.flags.vehicle_at_rest || !recent_terrain_aiding) {
@@ -425,24 +92,20 @@ void Ekf::controlHaglFakeFusion()
bool Ekf::isTerrainEstimateValid() const
{
bool valid = false;
// Assume being valid when the uncertainty is small compared to the height above ground
bool valid = getTerrainVariance() < sq(0.1f * getHagl());
#if defined(CONFIG_EKF2_RANGE_FINDER)
// we have been fusing range finder measurements in the last 5 seconds
if (isRecent(_aid_src_terrain_range_finder.time_last_fuse, (uint64_t)5e6)) {
if (_hagl_sensor_status.flags.range_finder || !_control_status.flags.in_air) {
valid = true;
}
if (_hagl_sensor_status.flags.range_finder && isRecent(_aid_src_rng_hgt.time_last_fuse, (uint64_t)5e6)) {
valid = true;
}
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
// we have been fusing optical flow measurements for terrain estimation within the last 5 seconds
// this can only be the case if the main filter does not fuse optical flow
if (_hagl_sensor_status.flags.flow && isRecent(_aid_src_terrain_optical_flow.time_last_fuse, (uint64_t)5e6)) {
if (_hagl_sensor_status.flags.flow && isRecent(_aid_src_optical_flow.time_last_fuse, (uint64_t)5e6)) {
valid = true;
}
@@ -450,7 +113,3 @@ bool Ekf::isTerrainEstimateValid() const
return valid;
}
void Ekf::terrainHandleVerticalPositionReset(const float delta_z) {
_terrain_vpos += delta_z;
}
src/modules/ekf2/EKF2.cpp
+5 -52
View File
@@ -326,7 +326,6 @@ bool EKF2::multi_init(int imu, int mag)
// RNG advertise
if (_param_ekf2_rng_ctrl.get()) {
_estimator_aid_src_rng_hgt_pub.advertise();
_estimator_rng_hgt_bias_pub.advertise();
}
#endif // CONFIG_EKF2_RANGE_FINDER
@@ -728,10 +727,6 @@ void EKF2::Run()
PublishBaroBias(now);
#endif // CONFIG_EKF2_BAROMETER
#if defined(CONFIG_EKF2_RANGE_FINDER)
PublishRngHgtBias(now);
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_EXTERNAL_VISION)
PublishEvPosBias(now);
#endif // CONFIG_EKF2_EXTERNAL_VISION
@@ -928,21 +923,6 @@ void EKF2::PublishAidSourceStatus(const hrt_abstime &timestamp)
// optical flow
PublishAidSourceStatus(_ekf.aid_src_optical_flow(), _status_optical_flow_pub_last, _estimator_aid_src_optical_flow_pub);
#endif // CONFIG_EKF2_OPTICAL_FLOW
#if defined(CONFIG_EKF2_TERRAIN)
# if defined(CONFIG_EKF2_RANGE_FINDER)
// range finder
PublishAidSourceStatus(_ekf.aid_src_terrain_range_finder(), _status_terrain_range_finder_pub_last,
_estimator_aid_src_terrain_range_finder_pub);
#endif // CONFIG_EKF2_RANGE_FINDER
# if defined(CONFIG_EKF2_OPTICAL_FLOW)
// optical flow
PublishAidSourceStatus(_ekf.aid_src_terrain_optical_flow(), _status_terrain_optical_flow_pub_last,
_estimator_aid_src_terrain_optical_flow_pub);
# endif // CONFIG_EKF2_OPTICAL_FLOW
#endif // CONFIG_EKF2_TERRAIN
}
void EKF2::PublishAttitude(const hrt_abstime &timestamp)
@@ -996,21 +976,6 @@ void EKF2::PublishGnssHgtBias(const hrt_abstime &timestamp)
}
#endif // CONFIG_EKF2_GNSS
#if defined(CONFIG_EKF2_RANGE_FINDER)
void EKF2::PublishRngHgtBias(const hrt_abstime &timestamp)
{
if (_ekf.get_rng_sample_delayed().time_us != 0) {
const BiasEstimator::status &status = _ekf.getRngHgtBiasEstimatorStatus();
if (fabsf(status.bias - _last_rng_hgt_bias_published) > 0.001f) {
_estimator_rng_hgt_bias_pub.publish(fillEstimatorBiasMsg(status, _ekf.get_rng_sample_delayed().time_us, timestamp));
_last_rng_hgt_bias_published = status.bias;
}
}
}
#endif // CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_EXTERNAL_VISION)
void EKF2::PublishEvPosBias(const hrt_abstime &timestamp)
{
@@ -1272,10 +1237,6 @@ void EKF2::PublishInnovations(const hrt_abstime &timestamp)
// Optical flow
innovations.flow[0] = _ekf.aid_src_optical_flow().innovation[0];
innovations.flow[1] = _ekf.aid_src_optical_flow().innovation[1];
# if defined(CONFIG_EKF2_TERRAIN)
innovations.terr_flow[0] = _ekf.aid_src_terrain_optical_flow().innovation[0];
innovations.terr_flow[1] = _ekf.aid_src_terrain_optical_flow().innovation[1];
# endif // CONFIG_EKF2_TERRAIN
#endif // CONFIG_EKF2_OPTICAL_FLOW
// heading
@@ -1313,7 +1274,7 @@ void EKF2::PublishInnovations(const hrt_abstime &timestamp)
#if defined(CONFIG_EKF2_TERRAIN) && defined(CONFIG_EKF2_RANGE_FINDER)
// hagl
innovations.hagl = _ekf.aid_src_terrain_range_finder().innovation;
innovations.hagl = _ekf.aid_src_rng_hgt().innovation;
#endif // CONFIG_EKF2_TERRAIN && CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_RANGE_FINDER)
@@ -1339,7 +1300,7 @@ void EKF2::PublishInnovations(const hrt_abstime &timestamp)
#if defined(CONFIG_EKF2_TERRAIN) && defined(CONFIG_EKF2_OPTICAL_FLOW)
// set dist bottom to scale flow innovation
const float dist_bottom = _ekf.getTerrainVertPos() - _ekf.getPosition()(2);
const float dist_bottom = _ekf.getHagl();
_preflt_checker.setDistBottom(dist_bottom);
#endif // CONFIG_EKF2_TERRAIN && CONFIG_EKF2_OPTICAL_FLOW
@@ -1406,10 +1367,6 @@ void EKF2::PublishInnovationTestRatios(const hrt_abstime &timestamp)
// Optical flow
test_ratios.flow[0] = _ekf.aid_src_optical_flow().test_ratio[0];
test_ratios.flow[1] = _ekf.aid_src_optical_flow().test_ratio[1];
# if defined(CONFIG_EKF2_TERRAIN)
test_ratios.terr_flow[0] = _ekf.aid_src_terrain_optical_flow().test_ratio[0];
test_ratios.terr_flow[1] = _ekf.aid_src_terrain_optical_flow().test_ratio[1];
# endif // CONFIG_EKF2_TERRAIN
#endif // CONFIG_EKF2_OPTICAL_FLOW
// heading
@@ -1447,7 +1404,7 @@ void EKF2::PublishInnovationTestRatios(const hrt_abstime &timestamp)
#if defined(CONFIG_EKF2_TERRAIN) && defined(CONFIG_EKF2_RANGE_FINDER)
// hagl
test_ratios.hagl = _ekf.aid_src_terrain_range_finder().test_ratio;
test_ratios.hagl = _ekf.aid_src_rng_hgt().test_ratio;
#endif // CONFIG_EKF2_TERRAIN && CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_RANGE_FINDER)
@@ -1503,10 +1460,6 @@ void EKF2::PublishInnovationVariances(const hrt_abstime &timestamp)
// Optical flow
variances.flow[0] = _ekf.aid_src_optical_flow().innovation_variance[0];
variances.flow[1] = _ekf.aid_src_optical_flow().innovation_variance[1];
# if defined(CONFIG_EKF2_TERRAIN)
variances.terr_flow[0] = _ekf.aid_src_terrain_optical_flow().innovation_variance[0];
variances.terr_flow[1] = _ekf.aid_src_terrain_optical_flow().innovation_variance[1];
# endif // CONFIG_EKF2_TERRAIN
#endif // CONFIG_EKF2_OPTICAL_FLOW
// heading
@@ -1544,7 +1497,7 @@ void EKF2::PublishInnovationVariances(const hrt_abstime &timestamp)
#if defined(CONFIG_EKF2_TERRAIN) && defined(CONFIG_EKF2_RANGE_FINDER)
// hagl
variances.hagl = _ekf.aid_src_terrain_range_finder().innovation_variance;
variances.hagl = _ekf.aid_src_rng_hgt().innovation_variance;
#endif // CONFIG_EKF2_TERRAIN && CONFIG_EKF2_RANGE_FINDER
#if defined(CONFIG_EKF2_RANGE_FINDER)
@@ -1620,7 +1573,7 @@ void EKF2::PublishLocalPosition(const hrt_abstime &timestamp)
#if defined(CONFIG_EKF2_TERRAIN)
// Distance to bottom surface (ground) in meters, must be positive
lpos.dist_bottom = math::max(_ekf.getTerrainVertPos() - lpos.z, 0.f);
lpos.dist_bottom = math::max(_ekf.getHagl(), 0.f);
lpos.dist_bottom_valid = _ekf.isTerrainEstimateValid();
lpos.dist_bottom_sensor_bitfield = _ekf.getTerrainEstimateSensorBitfield();
#endif // CONFIG_EKF2_TERRAIN
src/modules/ekf2/EKF2.hpp
-15
View File
@@ -337,19 +337,6 @@ private:
hrt_abstime _status_aux_vel_pub_last{0};
#endif // CONFIG_EKF2_AUXVEL
#if defined(CONFIG_EKF2_TERRAIN)
# if defined(CONFIG_EKF2_RANGE_FINDER)
uORB::PublicationMulti<estimator_aid_source1d_s> _estimator_aid_src_terrain_range_finder_pub {ORB_ID(estimator_aid_src_terrain_range_finder)};
hrt_abstime _status_terrain_range_finder_pub_last{0};
# endif // CONFIG_EKF2_RANGE_FINDER
# if defined(CONFIG_EKF2_OPTICAL_FLOW)
uORB::PublicationMulti<estimator_aid_source2d_s> _estimator_aid_src_terrain_optical_flow_pub {ORB_ID(estimator_aid_src_terrain_optical_flow)};
hrt_abstime _status_terrain_optical_flow_pub_last{0};
# endif // CONFIG_EKF2_OPTICAL_FLOW
#endif // CONFIG_EKF2_TERRAIN
#if defined(CONFIG_EKF2_OPTICAL_FLOW)
uORB::Subscription _vehicle_optical_flow_sub {ORB_ID(vehicle_optical_flow)};
uORB::PublicationMulti<vehicle_optical_flow_vel_s> _estimator_optical_flow_vel_pub{ORB_ID(estimator_optical_flow_vel)};
@@ -407,9 +394,7 @@ private:
#if defined(CONFIG_EKF2_RANGE_FINDER)
hrt_abstime _status_rng_hgt_pub_last {0};
float _last_rng_hgt_bias_published{};
uORB::PublicationMulti<estimator_bias_s> _estimator_rng_hgt_bias_pub {ORB_ID(estimator_rng_hgt_bias)};
uORB::PublicationMulti<estimator_aid_source1d_s> _estimator_aid_src_rng_hgt_pub{ORB_ID(estimator_aid_src_rng_hgt)};
uORB::SubscriptionMultiArray<distance_sensor_s> _distance_sensor_subs{ORB_ID::distance_sensor};
src/modules/ekf2/test/CMakeLists.txt
+1
View File
@@ -41,6 +41,7 @@ px4_add_unit_gtest(SRC test_EKF_airspeed.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
px4_add_unit_gtest(SRC test_EKF_basics.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
px4_add_unit_gtest(SRC test_EKF_externalVision.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
px4_add_unit_gtest(SRC test_EKF_flow.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
px4_add_unit_gtest(SRC test_EKF_flow_generated.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
px4_add_unit_gtest(SRC test_EKF_gyroscope.cpp LINKLIBS ecl_EKF ecl_sensor_sim)
px4_add_unit_gtest(SRC test_EKF_fusionLogic.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
px4_add_unit_gtest(SRC test_EKF_gps.cpp LINKLIBS ecl_EKF ecl_sensor_sim ecl_test_helper)
src/modules/ekf2/test/change_indication/ekf_gsf_reset.csv
+390 -390
View File
@@ -1,391 +1,391 @@
Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7],state[8],state[9],state[10],state[11],state[12],state[13],state[14],state[15],state[16],state[17],state[18],state[19],state[20],state[21],state[22],state[23],variance[0],variance[1],variance[2],variance[3],variance[4],variance[5],variance[6],variance[7],variance[8],variance[9],variance[10],variance[11],variance[12],variance[13],variance[14],variance[15],variance[16],variance[17],variance[18],variance[19],variance[20],variance[21],variance[22],variance[23]
10000,1,-0.0094,-0.01,-3.2e-06,0.00023,7.3e-05,-0.011,7.2e-06,2.2e-06,-0.00045,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.01,8.1e-05,25,25,10,1e+02,1e+02,1e+02,0.01,0.01,0.01,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
90000,1,-0.0094,-0.011,6.9e-05,-0.00047,0.0026,-0.026,-1.6e-05,0.00011,-0.0023,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.01,0.00036,25,25,10,1e+02,1e+02,1e+02,0.01,0.01,0.01,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
190000,1,-0.0094,-0.011,2.8e-05,6.9e-05,0.004,-0.041,-5.9e-06,0.00043,-0.0044,-3e-11,-2.7e-12,5.6e-13,0,0,-5e-08,0,0,0,0,0,0,0,0,0.011,0.011,0.00084,25,25,10,1e+02,1e+02,1,0.01,0.01,0.01,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
290000,1,-0.0094,-0.011,6.3e-05,0.001,0.0064,-0.053,3.8e-05,0.00036,-0.007,9.1e-10,1e-10,-1.7e-11,0,0,-2.5e-06,0,0,0,0,0,0,0,0,0.012,0.012,0.00075,25,25,9.6,0.37,0.37,0.41,0.01,0.01,0.0049,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
390000,1,-0.0094,-0.011,7e-05,0.0024,0.0083,-0.059,0.00021,0.0011,-0.0094,-1.1e-08,2.8e-09,2.9e-10,0,0,-1.5e-05,0,0,0,0,0,0,0,0,0.012,0.012,0.0012,25,25,8.1,0.97,0.97,0.32,0.01,0.01,0.0049,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
490000,1,-0.0095,-0.012,2e-05,0.0039,0.0048,-0.06,0.00024,0.00049,-0.011,1.6e-06,-3.7e-07,-4.2e-08,0,0,-4.1e-05,0,0,0,0,0,0,0,0,0.013,0.013,0.00072,7.8,7.8,5.9,0.34,0.34,0.31,0.01,0.01,0.0021,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
590000,1,-0.0095,-0.012,2.6e-05,0.006,0.0073,-0.059,0.00074,0.0011,-0.012,1.6e-06,-3.4e-07,-4e-08,0,0,-7.3e-05,0,0,0,0,0,0,0,0,0.015,0.015,0.00099,7.9,7.9,4.2,0.67,0.67,0.32,0.01,0.01,0.0021,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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790000,1,-0.0097,-0.013,9.9e-05,0.0086,0.0073,-0.063,0.0012,0.0012,-0.014,5.4e-06,-3.1e-06,-1.8e-07,0,0,-0.00016,0,0,0,0,0,0,0,0,0.018,0.018,0.00077,2.8,2.8,2,0.42,0.42,0.28,0.01,0.01,0.00098,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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1490000,1,-0.01,-0.014,0.00015,0.024,0.0029,-0.16,0.0038,0.00083,-0.093,0.00015,-0.00045,-1.2e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.027,0.027,0.00033,0.96,0.96,2,0.14,0.14,2.1,0.0088,0.0088,0.00014,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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1690000,1,-0.011,-0.014,0.00012,0.028,-9.5e-05,-0.19,0.0045,0.00063,-0.13,0.0002,-0.00088,-2.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.026,0.026,0.0003,1,1,2,0.14,0.14,3,0.0078,0.0078,0.0001,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
1790000,1,-0.011,-0.014,9.5e-05,0.035,-0.0019,-0.2,0.0076,0.00054,-0.15,0.0002,-0.00088,-2.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.028,0.028,0.00033,1.3,1.3,2,0.2,0.2,3.5,0.0078,0.0078,0.0001,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
1890000,1,-0.011,-0.015,7.5e-05,0.043,-0.0032,-0.22,0.011,0.00029,-0.17,0.0002,-0.00088,-2.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.031,0.031,0.00037,1.7,1.7,2,0.31,0.31,4.1,0.0078,0.0078,0.0001,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
1990000,1,-0.011,-0.014,8.5e-05,0.036,-0.0046,-0.23,0.0082,-0.00027,-0.19,0.00022,-0.0014,-3.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.025,0.025,0.00029,1.3,1.3,2.1,0.2,0.2,4.7,0.0067,0.0067,7.5e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2090000,1,-0.011,-0.014,4.7e-05,0.041,-0.0071,-0.24,0.012,-0.00085,-0.22,0.00022,-0.0014,-3.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.027,0.027,0.00032,1.7,1.7,2.1,0.31,0.31,5.3,0.0067,0.0067,7.5e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2190000,1,-0.011,-0.014,5.9e-05,0.033,-0.0068,-0.26,0.0079,-0.00096,-0.24,0.00017,-0.002,-4.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.02,0.02,0.00027,1.2,1.2,2.1,0.2,0.2,6,0.0055,0.0055,5.7e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2290000,1,-0.011,-0.014,4.5e-05,0.039,-0.0093,-0.27,0.011,-0.0017,-0.27,0.00017,-0.002,-4.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.022,0.022,0.00029,1.5,1.5,2.1,0.3,0.3,6.7,0.0055,0.0055,5.7e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2390000,1,-0.011,-0.013,6.2e-05,0.03,-0.0087,-0.29,0.0074,-0.0015,-0.3,9e-05,-0.0025,-5e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.017,0.017,0.00024,1,1,2.1,0.19,0.19,7.4,0.0046,0.0046,4.5e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2490000,1,-0.011,-0.013,4.4e-05,0.035,-0.011,-0.3,0.011,-0.0024,-0.32,9e-05,-0.0025,-5e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.018,0.018,0.00026,1.3,1.3,2.1,0.28,0.28,8.2,0.0046,0.0046,4.5e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2590000,1,-0.011,-0.013,5.9e-05,0.026,-0.009,-0.31,0.0068,-0.0018,-0.36,-1.4e-05,-0.0029,-5.7e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.014,0.014,0.00022,0.89,0.89,2.1,0.18,0.18,9.1,0.0038,0.0038,3.6e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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2790000,1,-0.011,-0.013,4.9e-05,0.023,-0.0093,-0.34,0.0062,-0.0019,-0.42,-0.00012,-0.0033,-6.1e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.011,0.011,0.00021,0.77,0.77,2.2,0.16,0.16,11,0.0032,0.0032,2.9e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2890000,1,-0.011,-0.013,-1.1e-06,0.027,-0.011,-0.35,0.0087,-0.0029,-0.46,-0.00012,-0.0033,-6.1e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.013,0.013,0.00022,0.95,0.95,2.2,0.23,0.23,12,0.0032,0.0032,2.9e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
2990000,1,-0.011,-0.013,4.6e-05,0.022,-0.0095,-0.36,0.0057,-0.0021,-0.49,-0.00023,-0.0036,-6.5e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.0099,0.0099,0.00019,0.67,0.67,2.2,0.15,0.15,13,0.0027,0.0027,2.4e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
3090000,1,-0.011,-0.013,4.9e-05,0.025,-0.011,-0.38,0.008,-0.0031,-0.53,-0.00023,-0.0036,-6.5e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.011,0.011,0.00021,0.83,0.83,2.2,0.22,0.22,14,0.0027,0.0027,2.4e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
3190000,1,-0.011,-0.013,-8e-06,0.02,-0.0086,-0.39,0.0053,-0.0021,-0.57,-0.00034,-0.0039,-6.7e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.0087,0.0087,0.00018,0.59,0.59,2.3,0.14,0.14,15,0.0023,0.0023,2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
3290000,1,-0.011,-0.013,3.2e-05,0.023,-0.01,-0.4,0.0074,-0.003,-0.61,-0.00034,-0.0039,-6.7e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.0096,0.0096,0.00019,0.73,0.73,2.3,0.2,0.2,16,0.0023,0.0023,2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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3590000,1,-0.011,-0.012,1.9e-05,0.017,-0.011,-0.44,0.0047,-0.0023,-0.73,-0.00055,-0.0044,-7.1e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.007,0.007,0.00016,0.49,0.49,2.4,0.13,0.13,20,0.0017,0.0017,1.4e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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3790000,1,-0.011,-0.012,0.00019,0.016,-0.013,-0.47,0.0044,-0.0026,-0.82,-0.00067,-0.0046,-7.2e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.0064,0.0064,0.00015,0.45,0.45,2.4,0.12,0.12,23,0.0014,0.0014,1.2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1
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2190000,1,-0.011,-0.014,5.9e-05,0.033,-0.0068,-0.26,0.0079,-0.00096,-0.24,0.00017,-0.002,-4.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.1,0.02,0.02,0.00027,1.2,1.2,2.1,0.2,0.2,6,0.0055,0.0055,5.7e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.58
2290000,1,-0.011,-0.014,4.5e-05,0.039,-0.0093,-0.27,0.011,-0.0017,-0.27,0.00017,-0.002,-4.2e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.1,0.022,0.022,0.00029,1.5,1.5,2.1,0.3,0.3,6.7,0.0055,0.0055,5.7e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.61
2390000,1,-0.011,-0.013,6.2e-05,0.03,-0.0087,-0.29,0.0074,-0.0015,-0.3,9e-05,-0.0025,-5e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.1,0.017,0.017,0.00024,1,1,2.1,0.19,0.19,7.4,0.0046,0.0046,4.5e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.63
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2590000,1,-0.011,-0.013,5.9e-05,0.026,-0.009,-0.31,0.0068,-0.0018,-0.36,-1.4e-05,-0.0029,-5.7e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.1,0.014,0.014,0.00022,0.89,0.89,2.1,0.18,0.18,9.1,0.0038,0.0038,3.6e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.68
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2790000,1,-0.011,-0.013,4.9e-05,0.023,-0.0093,-0.34,0.0062,-0.0019,-0.42,-0.00012,-0.0033,-6.1e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.1,0.011,0.011,0.00021,0.77,0.77,2.2,0.16,0.16,11,0.0032,0.0032,2.9e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.73
2890000,1,-0.011,-0.013,-1.1e-06,0.027,-0.011,-0.35,0.0087,-0.0029,-0.46,-0.00012,-0.0033,-6.1e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.1,0.013,0.013,0.00022,0.95,0.95,2.2,0.23,0.23,12,0.0032,0.0032,2.9e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.76
2990000,1,-0.011,-0.013,4.6e-05,0.022,-0.0095,-0.36,0.0057,-0.0021,-0.49,-0.00023,-0.0036,-6.5e-05,0,0,-0.00018,0,0,0,0,0,0,0,0,0.1,0.0099,0.0099,0.00019,0.67,0.67,2.2,0.15,0.15,13,0.0027,0.0027,2.4e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.78
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3190000,1,-0.011,-0.013,-8e-06,0.02,-0.0086,-0.39,0.0053,-0.0021,-0.57,-0.00034,-0.0039,-6.7e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.1,0.0087,0.0087,0.00018,0.59,0.59,2.3,0.14,0.14,15,0.0023,0.0023,2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.83
3290000,1,-0.011,-0.013,3.2e-05,0.023,-0.01,-0.4,0.0074,-0.003,-0.61,-0.00034,-0.0039,-6.7e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.1,0.0096,0.0096,0.00019,0.73,0.73,2.3,0.2,0.2,16,0.0023,0.0023,2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.86
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3590000,1,-0.011,-0.012,1.9e-05,0.017,-0.011,-0.44,0.0047,-0.0023,-0.73,-0.00055,-0.0044,-7.1e-05,0,0,-0.00017,0,0,0,0,0,0,0,0,0.1,0.007,0.007,0.00016,0.49,0.49,2.4,0.13,0.13,20,0.0017,0.0017,1.4e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.93
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3790000,1,-0.011,-0.012,0.00019,0.016,-0.013,-0.47,0.0044,-0.0026,-0.82,-0.00067,-0.0046,-7.2e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.1,0.0064,0.0064,0.00015,0.45,0.45,2.4,0.12,0.12,23,0.0014,0.0014,1.2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,0.98
3890000,1,-0.011,-0.012,0.00015,0.017,-0.014,-0.48,0.006,-0.0039,-0.87,-0.00067,-0.0046,-7.2e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.1,0.0069,0.0069,0.00016,0.55,0.55,2.4,0.17,0.17,24,0.0014,0.0014,1.2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1
3990000,1,-0.011,-0.012,0.00016,0.02,-0.016,-0.5,0.0079,-0.0055,-0.92,-0.00067,-0.0046,-7.2e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.1,0.0075,0.0075,0.00017,0.67,0.67,2.5,0.23,0.23,26,0.0014,0.0014,1.2e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1
4090000,1,-0.011,-0.012,0.00015,0.017,-0.014,-0.51,0.0056,-0.0041,-0.97,-0.00079,-0.0047,-7.3e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.1,0.0062,0.0062,0.00015,0.51,0.51,2.5,0.16,0.16,28,0.0012,0.0012,1e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.1
4190000,1,-0.011,-0.012,0.00013,0.02,-0.016,-0.53,0.0075,-0.0056,-1,-0.00079,-0.0047,-7.3e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.1,0.0067,0.0067,0.00016,0.61,0.61,2.5,0.21,0.21,29,0.0012,0.0012,1e-05,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.1
4290000,1,-0.01,-0.012,8.1e-05,0.017,-0.012,-0.54,0.0054,-0.0041,-1.1,-0.00091,-0.0049,-7.3e-05,0,0,-0.00016,0,0,0,0,0,0,0,0,0.1,0.0056,0.0056,0.00014,0.47,0.47,2.6,0.15,0.15,31,0.00097,0.00097,9e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.1
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4490000,1,-0.01,-0.012,0.00016,0.014,-0.0097,-0.57,0.0051,-0.0037,-1.2,-0.001,-0.005,-7.3e-05,0,0,-0.00015,0,0,0,0,0,0,0,0,0.1,0.0049,0.0049,0.00014,0.43,0.43,2.6,0.14,0.14,34,0.0008,0.0008,7.8e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.2
4590000,1,-0.01,-0.012,0.00019,0.017,-0.011,-0.58,0.0067,-0.0047,-1.2,-0.001,-0.005,-7.3e-05,0,0,-0.00015,0,0,0,0,0,0,0,0,0.1,0.0053,0.0053,0.00014,0.52,0.52,2.7,0.19,0.19,36,0.0008,0.0008,7.8e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.2
4690000,1,-0.01,-0.012,0.00019,0.014,-0.0096,-0.6,0.0048,-0.0033,-1.3,-0.0011,-0.0052,-7.4e-05,0,0,-0.00015,0,0,0,0,0,0,0,0,0.1,0.0044,0.0044,0.00013,0.4,0.4,2.7,0.14,0.14,38,0.00065,0.00065,6.9e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.2
4790000,1,-0.01,-0.012,0.00018,0.015,-0.011,-0.61,0.0062,-0.0043,-1.4,-0.0011,-0.0052,-7.4e-05,0,0,-0.00015,0,0,0,0,0,0,0,0,0.1,0.0047,0.0047,0.00014,0.48,0.48,2.7,0.18,0.18,40,0.00065,0.00065,6.9e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.2
4890000,1,-0.01,-0.011,0.00017,0.012,-0.0097,-0.63,0.0044,-0.0031,-1.4,-0.0012,-0.0053,-7.4e-05,0,0,-0.00014,0,0,0,0,0,0,0,0,0.1,0.0039,0.0039,0.00012,0.37,0.37,2.8,0.13,0.13,42,0.00053,0.00053,6e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.3
4990000,1,-0.01,-0.012,0.00015,0.015,-0.01,-0.64,0.0058,-0.0041,-1.5,-0.0012,-0.0053,-7.4e-05,0,0,-0.00014,0,0,0,0,0,0,0,0,0.1,0.0041,0.0041,0.00013,0.44,0.44,2.8,0.17,0.17,44,0.00053,0.00053,6e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.3
5090000,1,-0.01,-0.011,0.0002,0.011,-0.0081,-0.66,0.0041,-0.003,-1.6,-0.0013,-0.0054,-7.4e-05,0,0,-0.00014,0,0,0,0,0,0,0,0,0.1,0.0034,0.0034,0.00012,0.34,0.34,2.8,0.12,0.12,47,0.00043,0.00043,5.3e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.3
5190000,1,-0.01,-0.011,0.00022,0.013,-0.0095,-0.67,0.0053,-0.0039,-1.6,-0.0013,-0.0054,-7.4e-05,0,0,-0.00014,0,0,0,0,0,0,0,0,0.1,0.0036,0.0036,0.00012,0.4,0.4,2.9,0.16,0.16,49,0.00043,0.00043,5.3e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.3
5290000,1,-0.0099,-0.011,0.00021,0.0086,-0.007,-0.68,0.0037,-0.0027,-1.7,-0.0013,-0.0055,-7.4e-05,0,0,-0.00014,0,0,0,0,0,0,0,0,0.1,0.003,0.003,0.00011,0.31,0.31,2.9,0.12,0.12,51,0.00034,0.00034,4.7e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.4
5390000,1,-0.0099,-0.011,0.00027,0.0081,-0.0078,-0.7,0.0045,-0.0035,-1.8,-0.0013,-0.0055,-7.4e-05,0,0,-0.00014,0,0,0,0,0,0,0,0,0.1,0.0032,0.0032,0.00012,0.36,0.36,3,0.16,0.16,54,0.00034,0.00034,4.7e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.4
5490000,1,-0.0098,-0.011,0.00028,0.0055,-0.0059,-0.71,0.0031,-0.0025,-1.8,-0.0014,-0.0055,-7.4e-05,0,0,-0.00013,0,0,0,0,0,0,0,0,0.1,0.0026,0.0026,0.00011,0.28,0.28,3,0.11,0.11,56,0.00028,0.00028,4.2e-06,0.04,0.04,0.04,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,1,1,1.4
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18790000,0.78,-0.017,-0.0029,-0.62,-0.022,-0.021,0.004,-0.012,-0.012,-3.7e+02,-0.0012,-0.006,-4.3e-05,0.062,-0.035,-0.13,-0.11,-0.026,0.5,-0.0035,-0.092,-0.069,0,0,-3.6e+02,0.00011,0.00012,0.049,0.015,0.016,0.013,0.044,0.044,0.045,9.4e-07,1.1e-06,2.3e-06,0.0073,0.008,0.0007,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.7
18890000,0.78,-0.016,-0.003,-0.62,-0.022,-0.024,0.0046,-0.013,-0.015,-3.7e+02,-0.0013,-0.006,-4e-05,0.062,-0.034,-0.13,-0.11,-0.026,0.5,-0.0034,-0.092,-0.069,0,0,-3.6e+02,0.00011,0.00012,0.049,0.016,0.017,0.013,0.048,0.048,0.045,9.2e-07,1.1e-06,2.3e-06,0.0072,0.008,0.00068,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.8
18990000,0.78,-0.016,-0.003,-0.62,-0.019,-0.023,0.0033,-0.0093,-0.013,-3.7e+02,-0.0013,-0.006,-3.6e-05,0.061,-0.034,-0.13,-0.11,-0.026,0.5,-0.0033,-0.092,-0.069,0,0,-3.6e+02,0.00011,0.00011,0.049,0.015,0.015,0.012,0.043,0.043,0.044,9e-07,1.1e-06,2.3e-06,0.0072,0.0079,0.00065,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.8
19090000,0.78,-0.016,-0.003,-0.62,-0.019,-0.025,0.0063,-0.011,-0.015,-3.7e+02,-0.0013,-0.006,-3.6e-05,0.06,-0.033,-0.13,-0.11,-0.026,0.5,-0.0033,-0.092,-0.069,0,0,-3.6e+02,0.00011,0.00011,0.049,0.016,0.016,0.012,0.046,0.047,0.044,8.9e-07,1.1e-06,2.3e-06,0.0071,0.0079,0.00063,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.8
19190000,0.78,-0.016,-0.003,-0.62,-0.015,-0.024,0.0063,-0.0072,-0.014,-3.7e+02,-0.0013,-0.006,-3.2e-05,0.06,-0.033,-0.13,-0.11,-0.026,0.5,-0.0033,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.015,0.015,0.012,0.041,0.042,0.044,8.6e-07,1.1e-06,2.3e-06,0.0071,0.0078,0.0006,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.8
19290000,0.78,-0.016,-0.003,-0.62,-0.016,-0.024,0.0091,-0.0089,-0.016,-3.7e+02,-0.0013,-0.006,-3.3e-05,0.06,-0.034,-0.13,-0.11,-0.026,0.5,-0.0033,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.015,0.016,0.012,0.045,0.045,0.044,8.5e-07,1e-06,2.3e-06,0.007,0.0077,0.00058,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.9
19390000,0.78,-0.016,-0.003,-0.62,-0.015,-0.022,0.013,-0.008,-0.014,-3.7e+02,-0.0013,-0.006,-2.9e-05,0.06,-0.033,-0.13,-0.11,-0.026,0.5,-0.0033,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.014,0.015,0.012,0.04,0.041,0.043,8.3e-07,1e-06,2.3e-06,0.007,0.0077,0.00056,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.9
19490000,0.78,-0.016,-0.003,-0.62,-0.015,-0.024,0.0093,-0.0095,-0.017,-3.7e+02,-0.0013,-0.006,-2.6e-05,0.06,-0.033,-0.13,-0.11,-0.026,0.5,-0.0033,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.015,0.016,0.011,0.044,0.044,0.043,8.2e-07,1e-06,2.3e-06,0.0069,0.0076,0.00055,0.0011,7.2e-05,0.0012,0.00061,0.0012,0.0012,1,1,4.9
19590000,0.78,-0.016,-0.0031,-0.62,-0.013,-0.022,0.0085,-0.008,-0.015,-3.7e+02,-0.0013,-0.0059,-2e-05,0.06,-0.032,-0.13,-0.11,-0.026,0.5,-0.0032,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.014,0.015,0.011,0.04,0.04,0.042,8e-07,9.7e-07,2.3e-06,0.0069,0.0075,0.00052,0.0011,7.2e-05,0.0012,0.0006,0.0012,0.0012,1,1,4.9
19690000,0.78,-0.016,-0.0031,-0.62,-0.014,-0.021,0.01,-0.0088,-0.017,-3.7e+02,-0.0013,-0.006,-2.1e-05,0.059,-0.032,-0.13,-0.11,-0.026,0.5,-0.0032,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.015,0.016,0.011,0.043,0.044,0.042,7.8e-07,9.6e-07,2.3e-06,0.0068,0.0075,0.00051,0.0011,7.2e-05,0.0012,0.0006,0.0012,0.0012,1,1,5
19790000,0.78,-0.016,-0.0031,-0.62,-0.012,-0.018,0.01,-0.0075,-0.015,-3.7e+02,-0.0013,-0.006,-1.8e-05,0.059,-0.032,-0.13,-0.11,-0.026,0.5,-0.0032,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.014,0.014,0.011,0.039,0.039,0.042,7.7e-07,9.4e-07,2.3e-06,0.0068,0.0074,0.00049,0.0011,7.2e-05,0.0012,0.0006,0.0012,0.0012,1,1,5
19890000,0.78,-0.016,-0.0031,-0.62,-0.011,-0.02,0.012,-0.0091,-0.018,-3.7e+02,-0.0013,-0.0059,-1.4e-05,0.06,-0.032,-0.13,-0.11,-0.026,0.5,-0.0031,-0.092,-0.068,0,0,-3.6e+02,0.0001,0.00011,0.049,0.015,0.015,0.011,0.043,0.043,0.042,7.6e-07,9.3e-07,2.3e-06,0.0067,0.0074,0.00048,0.0011,7.2e-05,0.0012,0.0006,0.0012,0.0012,1,1,5
19990000,0.78,-0.016,-0.0032,-0.62,-0.0095,-0.02,0.014,-0.0079,-0.017,-3.7e+02,-0.0013,-0.0059,-4.8e-06,0.06,-0.031,-0.13,-0.11,-0.026,0.5,-0.003,-0.092,-0.068,0,0,-3.6e+02,9.9e-05,0.00011,0.049,0.014,0.014,0.01,0.039,0.039,0.041,7.4e-07,9e-07,2.3e-06,0.0067,0.0073,0.00046,0.0011,7.2e-05,0.0012,0.0006,0.0012,0.0012,1,1,5
20090000,0.78,-0.016,-0.0033,-0.62,-0.0095,-0.021,0.015,-0.0085,-0.02,-3.7e+02,-0.0013,-0.0059,-8.7e-08,0.061,-0.03,-0.13,-0.11,-0.026,0.5,-0.0029,-0.092,-0.068,0,0,-3.6e+02,9.9e-05,0.00011,0.049,0.014,0.015,0.01,0.042,0.042,0.042,7.3e-07,8.9e-07,2.3e-06,0.0066,0.0073,0.00045,0.0011,7.2e-05,0.0012,0.0006,0.0012,0.0012,1,1,5.1
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src/modules/ekf2/test/change_indication/iris_gps.csv
+350 -350
View File
@@ -1,351 +1,351 @@
Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7],state[8],state[9],state[10],state[11],state[12],state[13],state[14],state[15],state[16],state[17],state[18],state[19],state[20],state[21],state[22],state[23],variance[0],variance[1],variance[2],variance[3],variance[4],variance[5],variance[6],variance[7],variance[8],variance[9],variance[10],variance[11],variance[12],variance[13],variance[14],variance[15],variance[16],variance[17],variance[18],variance[19],variance[20],variance[21],variance[22],variance[23]
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345 34290000,-0.3,0.015,-0.0053,0.95,0.04,0.1,-0.06,0.098,-0.0048,-0.045,-0.0013,-0.0057,-3e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.084,-0.032,-0.067,0,0,4.1e-05,3.6e-05,0.047,0.021,0.023,0.005,0.37,0.37,0.03,2.5e-07,2.4e-07,1.4e-06,0.0018,0.002,7.6e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1 34290000,-0.3,0.015,-0.0053,0.95,0.04,0.1,-0.06,0.098,-0.0048,-0.045,-0.0013,-0.0057,-3e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.084,-0.032,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.047,0.021,0.023,0.005,0.37,0.37,0.03,2.5e-07,2.4e-07,1.4e-06,0.0018,0.002,7.6e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.26
346 34390000,-0.3,0.015,-0.0052,0.95,0.042,0.097,-0.055,0.093,-0.016,-0.049,-0.0013,-0.0057,-3.8e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.084,-0.032,-0.067,0,0,4.1e-05,3.6e-05,0.047,0.02,0.022,0.005,0.37,0.37,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.6e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1 34390000,-0.3,0.015,-0.0052,0.95,0.042,0.097,-0.055,0.093,-0.016,-0.049,-0.0013,-0.0057,-3.9e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.084,-0.032,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.047,0.02,0.022,0.005,0.37,0.37,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.6e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.28
347 34490000,-0.3,0.015,-0.0052,0.95,0.045,0.1,-0.053,0.097,-0.0069,-0.052,-0.0013,-0.0057,-3e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.032,-0.067,0,0,4.1e-05,3.6e-05,0.047,0.021,0.022,0.005,0.38,0.38,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.6e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1 34490000,-0.3,0.015,-0.0052,0.95,0.045,0.1,-0.053,0.097,-0.0069,-0.052,-0.0013,-0.0057,-3e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.032,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.047,0.021,0.022,0.005,0.38,0.38,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.6e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.31
348 34590000,-0.3,0.014,-0.0051,0.95,0.045,0.098,0.74,0.092,-0.021,-0.024,-0.0013,-0.0057,-4e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,4.1e-05,3.6e-05,0.046,0.02,0.021,0.005,0.38,0.38,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1 34590000,-0.3,0.014,-0.0051,0.95,0.045,0.098,0.74,0.092,-0.021,-0.024,-0.0013,-0.0057,-4e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.046,0.02,0.021,0.005,0.38,0.38,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.33
349 34690000,-0.3,0.014,-0.0051,0.95,0.05,0.1,1.7,0.097,-0.011,0.095,-0.0013,-0.0057,-3.7e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,4.1e-05,3.6e-05,0.046,0.02,0.021,0.005,0.39,0.39,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1 34690000,-0.3,0.014,-0.0051,0.95,0.05,0.1,1.7,0.097,-0.011,0.095,-0.0013,-0.0057,-3.7e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.046,0.02,0.021,0.005,0.39,0.39,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.4e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.36
350 34790000,-0.3,0.014,-0.005,0.95,0.051,0.099,2.7,0.091,-0.025,0.27,-0.0013,-0.0057,-4.7e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,4.1e-05,3.6e-05,0.046,0.019,0.019,0.005,0.39,0.39,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.3e-05,0.0012,0.0011,0.00062,0.0012,1,1 34790000,-0.3,0.014,-0.005,0.95,0.051,0.099,2.7,0.091,-0.025,0.27,-0.0013,-0.0057,-4.7e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.046,0.019,0.019,0.005,0.39,0.39,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.3e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.38
351 34890000,-0.3,0.014,-0.0051,0.95,0.056,0.1,3.7,0.096,-0.015,0.56,-0.0013,-0.0057,-4.3e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,4.1e-05,3.6e-05,0.046,0.019,0.019,0.005,0.4,0.4,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.3e-05,0.0012,0.0011,0.00062,0.0012,1,1 34890000,-0.3,0.014,-0.0051,0.95,0.056,0.1,3.7,0.096,-0.015,0.56,-0.0013,-0.0057,-4.3e-05,0.066,0.014,-0.12,-0.11,-0.025,0.5,0.085,-0.031,-0.067,0,0,0.12,4.1e-05,3.6e-05,0.046,0.019,0.019,0.005,0.4,0.4,0.03,2.5e-07,2.3e-07,1.4e-06,0.0018,0.002,7.5e-05,0.0011,6.3e-05,0.0012,0.0011,0.00062,0.0012,1,1,0.41
src/modules/ekf2/test/sensor_simulator/ekf_wrapper.cpp
-20
View File
@@ -240,16 +240,6 @@ bool EkfWrapper::isWindVelocityEstimated() const
return _ekf->control_status_flags().wind;
}
void EkfWrapper::enableTerrainRngFusion()
{
_ekf_params->terrain_fusion_mode |= TerrainFusionMask::TerrainFuseRangeFinder;
}
void EkfWrapper::disableTerrainRngFusion()
{
_ekf_params->terrain_fusion_mode &= ~TerrainFusionMask::TerrainFuseRangeFinder;
}
bool EkfWrapper::isIntendingTerrainRngFusion() const
{
terrain_fusion_status_u terrain_status;
@@ -257,16 +247,6 @@ bool EkfWrapper::isIntendingTerrainRngFusion() const
return terrain_status.flags.range_finder;
}
void EkfWrapper::enableTerrainFlowFusion()
{
_ekf_params->terrain_fusion_mode |= TerrainFusionMask::TerrainFuseOpticalFlow;
}
void EkfWrapper::disableTerrainFlowFusion()
{
_ekf_params->terrain_fusion_mode &= ~TerrainFusionMask::TerrainFuseOpticalFlow;
}
bool EkfWrapper::isIntendingTerrainFlowFusion() const
{
terrain_fusion_status_u terrain_status;
src/modules/ekf2/test/sensor_simulator/ekf_wrapper.h
-4
View File
@@ -110,12 +110,8 @@ public:
bool isWindVelocityEstimated() const;
void enableTerrainRngFusion();
void disableTerrainRngFusion();
bool isIntendingTerrainRngFusion() const;
void enableTerrainFlowFusion();
void disableTerrainFlowFusion();
bool isIntendingTerrainFlowFusion() const;
Eulerf getEulerAngles() const;
src/modules/ekf2/test/test_EKF_flow.cpp
+12 -12
View File
@@ -120,7 +120,7 @@ TEST_F(EkfFlowTest, resetToFlowVelocityInAir)
_sensor_simulator.runSeconds(5.f);
const float estimated_distance_to_ground = _ekf->getTerrainVertPos();
const float estimated_distance_to_ground = _ekf->getHagl();
EXPECT_FLOAT_EQ(estimated_distance_to_ground, simulated_distance_to_ground);
reset_logging_checker.capturePreResetState();
@@ -133,10 +133,7 @@ TEST_F(EkfFlowTest, resetToFlowVelocityInAir)
_ekf_wrapper.enableFlowFusion();
_sensor_simulator.startFlow();
// Let it reset but not fuse more measurements. We actually need to send 2
// samples to get a reset because the first one cannot be used as the gyro
// compensation needs to be accumulated between two samples.
_sensor_simulator.runTrajectorySeconds(0.14);
_sensor_simulator.runTrajectorySeconds(1);
// THEN: estimated velocity should match simulated velocity
const Vector3f estimated_velocity = _ekf->getVelocity();
@@ -144,7 +141,11 @@ TEST_F(EkfFlowTest, resetToFlowVelocityInAir)
simulated_velocity.print();
EXPECT_TRUE(isEqual(estimated_velocity, simulated_velocity))
<< "estimated vel = " << estimated_velocity(0) << ", "
<< estimated_velocity(1);
<< estimated_velocity(1) << "\n"
<< "simulated vel = " << simulated_velocity(0) << ", "
<< simulated_velocity(1);
EXPECT_NEAR(simulated_distance_to_ground, _ekf->getHagl(), 0.1f);
// AND: the reset in velocity should be saved correctly
reset_logging_checker.capturePostResetState();
@@ -158,7 +159,7 @@ TEST_F(EkfFlowTest, resetToFlowVelocityOnGround)
ResetLoggingChecker reset_logging_checker(_ekf);
// WHEN: being on ground
const float estimated_distance_to_ground = _ekf->getTerrainVertPos();
const float estimated_distance_to_ground = _ekf->getHagl();
EXPECT_LT(estimated_distance_to_ground, 0.3f);
reset_logging_checker.capturePreResetState();
@@ -177,11 +178,10 @@ TEST_F(EkfFlowTest, resetToFlowVelocityOnGround)
EXPECT_TRUE(isEqual(estimated_horz_velocity, Vector2f(0.f, 0.f)))
<< estimated_horz_velocity(0) << ", " << estimated_horz_velocity(1);
// AND: the reset in velocity should be saved correctly
// AND: the velocity isn't reset as it is already correct
reset_logging_checker.capturePostResetState();
EXPECT_TRUE(reset_logging_checker.isHorizontalVelocityResetCounterIncreasedBy(1));
EXPECT_TRUE(reset_logging_checker.isHorizontalVelocityResetCounterIncreasedBy(0));
EXPECT_TRUE(reset_logging_checker.isVerticalVelocityResetCounterIncreasedBy(0));
EXPECT_TRUE(reset_logging_checker.isVelocityDeltaLoggedCorrectly(1e-9f));
}
TEST_F(EkfFlowTest, inAirConvergence)
@@ -222,9 +222,9 @@ TEST_F(EkfFlowTest, inAirConvergence)
// THEN: estimated velocity should converge to the simulated velocity
// This takes a bit of time because the data is inconsistent with IMU measurements
estimated_velocity = _ekf->getVelocity();
EXPECT_NEAR(estimated_velocity(0), simulated_velocity(0), 0.05f)
EXPECT_NEAR(estimated_velocity(0), simulated_velocity(0), 0.01f)
<< "estimated vel = " << estimated_velocity(0);
EXPECT_NEAR(estimated_velocity(1), simulated_velocity(1), 0.05f)
EXPECT_NEAR(estimated_velocity(1), simulated_velocity(1), 0.01f)
<< estimated_velocity(1);
}
src/modules/ekf2/test/test_EKF_flow_generated.cpp
+73
View File
@@ -0,0 +1,73 @@
/****************************************************************************
*
* Copyright (C) 2024 PX4 Development Team. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in
* the documentation and/or other materials provided with the
* distribution.
* 3. Neither the name PX4 nor the names of its contributors may be
* used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
****************************************************************************/
#include <cfloat>
#include <gtest/gtest.h>
#include "EKF/ekf.h"
#include "test_helper/comparison_helper.h"
#include "../EKF/python/ekf_derivation/generated/compute_flow_xy_innov_var_and_hx.h"
#include "../EKF/python/ekf_derivation/generated/compute_flow_y_innov_var_and_h.h"
using namespace matrix;
TEST(FlowGenerated, distBottom0xy)
{
// GIVEN: 0 distance to the ground (singularity)
StateSample state{};
state.quat_nominal = Quatf();
const float R = sq(radians(sq(0.5f)));
SquareMatrixState P = createRandomCovarianceMatrix();
VectorState H;
Vector2f innov_var;
sym::ComputeFlowXyInnovVarAndHx(state.vector(), P, R, FLT_EPSILON, &innov_var, &H);
EXPECT_GT(innov_var(0), 1e12);
EXPECT_GT(innov_var(1), 1e12);
}
TEST(FlowGenerated, distBottom0y)
{
// GIVEN: 0 distance to the ground (singularity)
StateSample state{};
state.quat_nominal = Quatf();
const float R = sq(radians(sq(0.5f)));
SquareMatrixState P = createRandomCovarianceMatrix();
VectorState H;
float innov_var;
sym::ComputeFlowYInnovVarAndH(state.vector(), P, R, FLT_EPSILON, &innov_var, &H);
EXPECT_GT(innov_var, 1e12);
}
src/modules/ekf2/test/test_EKF_height_fusion.cpp
+9 -8
View File
@@ -60,6 +60,7 @@ public:
{
_ekf->init(0);
_ekf_wrapper.disableBaroHeightFusion();
_ekf_wrapper.disableRangeHeightFusion();
_sensor_simulator.runSeconds(0.1);
_ekf->set_in_air_status(false);
_ekf->set_vehicle_at_rest(true);
@@ -111,7 +112,7 @@ TEST_F(EkfHeightFusionTest, baroRef)
EXPECT_FALSE(_ekf_wrapper.isIntendingExternalVisionHeightFusion());
// AND WHEN: the baro data increases
const float baro_increment = 5.f;
const float baro_increment = 4.f;
_sensor_simulator._baro.setData(_sensor_simulator._baro.getData() + baro_increment);
_sensor_simulator.runSeconds(60);
@@ -124,8 +125,8 @@ TEST_F(EkfHeightFusionTest, baroRef)
const BiasEstimator::status &gps_status = _ekf->getGpsHgtBiasEstimatorStatus();
EXPECT_NEAR(gps_status.bias, -baro_increment, 0.2f);
const BiasEstimator::status &rng_status = _ekf->getRngHgtBiasEstimatorStatus();
EXPECT_NEAR(rng_status.bias, -baro_increment, 1.2f);
const float terrain = _ekf->getTerrainVertPos();
EXPECT_NEAR(terrain, -baro_increment, 1.2f);
const BiasEstimator::status &ev_status = _ekf->getEvHgtBiasEstimatorStatus();
EXPECT_EQ(ev_status.bias, 0.f);
@@ -150,8 +151,8 @@ TEST_F(EkfHeightFusionTest, baroRef)
// the estimated height follows the GPS height
EXPECT_NEAR(_ekf->getPosition()(2), -(baro_increment + gps_increment), 0.3f);
// and the range finder bias is adjusted to follow the new reference
const BiasEstimator::status &rng_status_2 = _ekf->getRngHgtBiasEstimatorStatus();
EXPECT_NEAR(rng_status_2.bias, -(baro_increment + gps_increment), 1.3f);
const float terrain2 = _ekf->getTerrainVertPos();
EXPECT_NEAR(terrain2, -(baro_increment + gps_increment), 1.3f);
}
TEST_F(EkfHeightFusionTest, gpsRef)
@@ -181,8 +182,8 @@ TEST_F(EkfHeightFusionTest, gpsRef)
const BiasEstimator::status &baro_status = _ekf->getBaroBiasEstimatorStatus();
EXPECT_NEAR(baro_status.bias, baro_initial + baro_increment, 1.3f);
const BiasEstimator::status &rng_status = _ekf->getRngHgtBiasEstimatorStatus();
EXPECT_NEAR(rng_status.bias, 0.f, 1.1f); // TODO: why?
const float terrain = _ekf->getTerrainVertPos();
EXPECT_NEAR(terrain, 0.f, 1.1f); // TODO: why?
// BUT WHEN: the GPS jumps by a lot
const float gps_step = 100.f;
@@ -290,7 +291,7 @@ TEST_F(EkfHeightFusionTest, changeEkfOriginAlt)
reset_logging_checker.capturePostResetState();
EXPECT_NEAR(_ekf->getBaroBiasEstimatorStatus().bias, _sensor_simulator._baro.getData() + alt_increment, 0.2f);
EXPECT_NEAR(_ekf->getRngHgtBiasEstimatorStatus().bias, alt_increment, 1.f);
EXPECT_NEAR(_ekf->getTerrainVertPos(), alt_increment, 1.f);
EXPECT_TRUE(reset_logging_checker.isVerticalVelocityResetCounterIncreasedBy(0));
EXPECT_TRUE(reset_logging_checker.isVerticalPositionResetCounterIncreasedBy(1));
}
src/modules/ekf2/test/test_EKF_terrain_estimator.cpp
+23 -17
View File
@@ -116,18 +116,22 @@ public:
TEST_F(EkfTerrainTest, setFlowAndRangeTerrainFusion)
{
// GIVEN: flow and range are enabled
_ekf_wrapper.enableFlowFusion();
_ekf_wrapper.enableRangeHeightFusion();
// WHEN: simulate being 5m above ground
const float simulated_distance_to_ground = 1.f;
runFlowAndRngScenario(simulated_distance_to_ground, simulated_distance_to_ground);
// THEN: By default, both rng and flow aiding are active
// THEN: both should start terrain aiding
EXPECT_TRUE(_ekf_wrapper.isIntendingTerrainRngFusion());
EXPECT_FALSE(_ekf_wrapper.isIntendingTerrainFlowFusion());
const float estimated_distance_to_ground = _ekf->getTerrainVertPos();
EXPECT_NEAR(estimated_distance_to_ground, simulated_distance_to_ground, 0.01);
EXPECT_TRUE(_ekf_wrapper.isIntendingTerrainFlowFusion());
const float estimated_distance_to_ground = _ekf->getHagl();
EXPECT_NEAR(estimated_distance_to_ground, simulated_distance_to_ground, 0.02);
// WHEN: rng fusion is disabled
_ekf_wrapper.disableTerrainRngFusion();
_ekf_wrapper.disableRangeHeightFusion();
_sensor_simulator.runSeconds(5.1);
// THEN: rng fusion should be disabled and flow fusion should take over
@@ -135,7 +139,7 @@ TEST_F(EkfTerrainTest, setFlowAndRangeTerrainFusion)
EXPECT_TRUE(_ekf_wrapper.isIntendingTerrainFlowFusion());
// WHEN: flow is now diabled
_ekf_wrapper.disableTerrainFlowFusion();
_ekf_wrapper.disableFlowFusion();
_sensor_simulator.runSeconds(0.2);
// THEN: flow is now also disabled
@@ -146,8 +150,8 @@ TEST_F(EkfTerrainTest, setFlowAndRangeTerrainFusion)
TEST_F(EkfTerrainTest, testFlowForTerrainFusion)
{
// GIVEN: flow for terrain enabled but not range finder
_ekf_wrapper.enableTerrainFlowFusion();
_ekf_wrapper.disableTerrainRngFusion();
_ekf_wrapper.enableFlowFusion();
_ekf_wrapper.disableRangeHeightFusion();
// WHEN: the sensors do not agree
const float rng_height = 1.f;
@@ -158,16 +162,17 @@ TEST_F(EkfTerrainTest, testFlowForTerrainFusion)
// should converge to the simulated height
EXPECT_FALSE(_ekf_wrapper.isIntendingTerrainRngFusion());
EXPECT_TRUE(_ekf_wrapper.isIntendingTerrainFlowFusion());
EXPECT_TRUE(_ekf->isTerrainEstimateValid());
const float estimated_distance_to_ground = _ekf->getTerrainVertPos();
EXPECT_NEAR(estimated_distance_to_ground, flow_height, 0.5f);
const float estimated_distance_to_ground = _ekf->getHagl();
EXPECT_NEAR(estimated_distance_to_ground, flow_height, 0.9f);
}
TEST_F(EkfTerrainTest, testRngForTerrainFusion)
{
// GIVEN: rng for terrain but not flow
_ekf_wrapper.disableTerrainFlowFusion();
_ekf_wrapper.enableTerrainRngFusion();
_ekf_wrapper.disableFlowFusion();
_ekf_wrapper.enableRangeHeightFusion();
// WHEN: the sensors do not agree
const float rng_height = 1.f;
@@ -178,22 +183,23 @@ TEST_F(EkfTerrainTest, testRngForTerrainFusion)
// should converge to the simulated height
EXPECT_TRUE(_ekf_wrapper.isIntendingTerrainRngFusion());
EXPECT_FALSE(_ekf_wrapper.isIntendingTerrainFlowFusion());
EXPECT_TRUE(_ekf->isTerrainEstimateValid());
const float estimated_distance_to_ground = _ekf->getTerrainVertPos();
const float estimated_distance_to_ground = _ekf->getHagl();
EXPECT_NEAR(estimated_distance_to_ground, rng_height, 0.01f);
}
TEST_F(EkfTerrainTest, testHeightReset)
{
// GIVEN: rng for terrain but not flow
_ekf_wrapper.disableTerrainFlowFusion();
_ekf_wrapper.enableTerrainRngFusion();
_ekf_wrapper.disableFlowFusion();
_ekf_wrapper.enableRangeHeightFusion();
const float rng_height = 1.f;
const float flow_height = 1.f;
runFlowAndRngScenario(rng_height, flow_height);
const float estimated_distance_to_ground = _ekf->getTerrainVertPos() - _ekf->getPosition()(2);
const float estimated_distance_to_ground = _ekf->getHagl();
ResetLoggingChecker reset_logging_checker(_ekf);
reset_logging_checker.capturePreResetState();
@@ -207,5 +213,5 @@ TEST_F(EkfTerrainTest, testHeightReset)
// THEN: a height reset occured and the estimated distance to the ground remains constant
reset_logging_checker.capturePostResetState();
EXPECT_TRUE(reset_logging_checker.isVerticalPositionResetCounterIncreasedBy(1));
EXPECT_NEAR(estimated_distance_to_ground, _ekf->getTerrainVertPos() - _ekf->getPosition()(2), 1e-3f);
EXPECT_NEAR(estimated_distance_to_ground, _ekf->getHagl(), 1e-3f);
}
src/modules/logger/logged_topics.cpp
-6
View File
@@ -165,7 +165,6 @@ void LoggedTopics::add_default_topics()
// always add the first instance
add_topic("estimator_baro_bias", 500);
add_topic("estimator_gnss_hgt_bias", 500);
add_topic("estimator_rng_hgt_bias", 500);
add_topic("estimator_ev_pos_bias", 500);
add_topic("estimator_event_flags", 0);
add_topic("estimator_gps_status", 1000);
@@ -181,7 +180,6 @@ void LoggedTopics::add_default_topics()
add_optional_topic_multi("estimator_baro_bias", 500, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_gnss_hgt_bias", 500, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_rng_hgt_bias", 500, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_ev_pos_bias", 500, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_event_flags", 0, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_gps_status", 1000, MAX_ESTIMATOR_INSTANCES);
@@ -209,7 +207,6 @@ void LoggedTopics::add_default_topics()
// add_optional_topic_multi("estimator_aid_src_gnss_pos", 100, MAX_ESTIMATOR_INSTANCES);
// add_optional_topic_multi("estimator_aid_src_mag", 100, MAX_ESTIMATOR_INSTANCES);
// add_optional_topic_multi("estimator_aid_src_optical_flow", 100, MAX_ESTIMATOR_INSTANCES);
// add_optional_topic_multi("estimator_aid_src_terrain_optical_flow", 100, MAX_ESTIMATOR_INSTANCES);
// add_optional_topic_multi("estimator_aid_src_ev_yaw", 100, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_aid_src_aux_global_position", 100, MAX_ESTIMATOR_INSTANCES);
@@ -267,7 +264,6 @@ void LoggedTopics::add_default_topics()
// EKF replay
add_topic("estimator_baro_bias");
add_topic("estimator_gnss_hgt_bias");
add_topic("estimator_rng_hgt_bias");
add_topic("estimator_ev_pos_bias");
add_topic("estimator_event_flags");
add_topic("estimator_gps_status");
@@ -301,8 +297,6 @@ void LoggedTopics::add_default_topics()
add_optional_topic_multi("estimator_aid_src_gravity", 0, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_aid_src_mag", 0, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_aid_src_optical_flow", 0, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_aid_src_terrain_optical_flow", 0, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_aid_src_terrain_range_finder", 0, MAX_ESTIMATOR_INSTANCES);
add_optional_topic_multi("estimator_aid_src_sideslip", 0, MAX_ESTIMATOR_INSTANCES);
#endif /* CONFIG_ARCH_BOARD_PX4_SITL */