fly316/PX4-Autopilot
7
0
Fork 0
mirror of https://gitee.com/mirrors_PX4/PX4-Autopilot.git synced 2026-06-30 10:30:36 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
9ccc1db64951f1f4a7314b0ca78e31597cbff39e
PX4-Autopilot/src/modules/ekf2/EKF2.cpp
T
Daniel Agar 9ccc1db649 estimator_status split out estimator_states
2020-09-04 10:48:26 -04:00

1998 lines
73 KiB
C++
Raw Blame History

/****************************************************************************
*
* Copyright (c) 2015-2020 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 "EKF2.hpp"
EKF2::EKF2(bool replay_mode):
ModuleParams(nullptr),
ScheduledWorkItem(MODULE_NAME, px4::wq_configurations::nav_and_controllers),
_replay_mode(replay_mode),
_ekf_update_perf(perf_alloc(PC_ELAPSED, MODULE_NAME": update")),
_params(_ekf.getParamHandle()),
_param_ekf2_min_obs_dt(_params->sensor_interval_min_ms),
_param_ekf2_mag_delay(_params->mag_delay_ms),
_param_ekf2_baro_delay(_params->baro_delay_ms),
_param_ekf2_gps_delay(_params->gps_delay_ms),
_param_ekf2_of_delay(_params->flow_delay_ms),
_param_ekf2_rng_delay(_params->range_delay_ms),
_param_ekf2_asp_delay(_params->airspeed_delay_ms),
_param_ekf2_ev_delay(_params->ev_delay_ms),
_param_ekf2_avel_delay(_params->auxvel_delay_ms),
_param_ekf2_gyr_noise(_params->gyro_noise),
_param_ekf2_acc_noise(_params->accel_noise),
_param_ekf2_gyr_b_noise(_params->gyro_bias_p_noise),
_param_ekf2_acc_b_noise(_params->accel_bias_p_noise),
_param_ekf2_mag_e_noise(_params->mage_p_noise),
_param_ekf2_mag_b_noise(_params->magb_p_noise),
_param_ekf2_wind_noise(_params->wind_vel_p_noise),
_param_ekf2_terr_noise(_params->terrain_p_noise),
_param_ekf2_terr_grad(_params->terrain_gradient),
_param_ekf2_gps_v_noise(_params->gps_vel_noise),
_param_ekf2_gps_p_noise(_params->gps_pos_noise),
_param_ekf2_noaid_noise(_params->pos_noaid_noise),
_param_ekf2_baro_noise(_params->baro_noise),
_param_ekf2_baro_gate(_params->baro_innov_gate),
_param_ekf2_gnd_eff_dz(_params->gnd_effect_deadzone),
_param_ekf2_gnd_max_hgt(_params->gnd_effect_max_hgt),
_param_ekf2_gps_p_gate(_params->gps_pos_innov_gate),
_param_ekf2_gps_v_gate(_params->gps_vel_innov_gate),
_param_ekf2_tas_gate(_params->tas_innov_gate),
_param_ekf2_head_noise(_params->mag_heading_noise),
_param_ekf2_mag_noise(_params->mag_noise),
_param_ekf2_eas_noise(_params->eas_noise),
_param_ekf2_beta_gate(_params->beta_innov_gate),
_param_ekf2_beta_noise(_params->beta_noise),
_param_ekf2_mag_decl(_params->mag_declination_deg),
_param_ekf2_hdg_gate(_params->heading_innov_gate),
_param_ekf2_mag_gate(_params->mag_innov_gate),
_param_ekf2_decl_type(_params->mag_declination_source),
_param_ekf2_mag_type(_params->mag_fusion_type),
_param_ekf2_mag_acclim(_params->mag_acc_gate),
_param_ekf2_mag_yawlim(_params->mag_yaw_rate_gate),
_param_ekf2_gps_check(_params->gps_check_mask),
_param_ekf2_req_eph(_params->req_hacc),
_param_ekf2_req_epv(_params->req_vacc),
_param_ekf2_req_sacc(_params->req_sacc),
_param_ekf2_req_nsats(_params->req_nsats),
_param_ekf2_req_pdop(_params->req_pdop),
_param_ekf2_req_hdrift(_params->req_hdrift),
_param_ekf2_req_vdrift(_params->req_vdrift),
_param_ekf2_aid_mask(_params->fusion_mode),
_param_ekf2_hgt_mode(_params->vdist_sensor_type),
_param_ekf2_terr_mask(_params->terrain_fusion_mode),
_param_ekf2_noaid_tout(_params->valid_timeout_max),
_param_ekf2_rng_noise(_params->range_noise),
_param_ekf2_rng_sfe(_params->range_noise_scaler),
_param_ekf2_rng_gate(_params->range_innov_gate),
_param_ekf2_min_rng(_params->rng_gnd_clearance),
_param_ekf2_rng_pitch(_params->rng_sens_pitch),
_param_ekf2_rng_aid(_params->range_aid),
_param_ekf2_rng_a_vmax(_params->max_vel_for_range_aid),
_param_ekf2_rng_a_hmax(_params->max_hagl_for_range_aid),
_param_ekf2_rng_a_igate(_params->range_aid_innov_gate),
_param_ekf2_evv_gate(_params->ev_vel_innov_gate),
_param_ekf2_evp_gate(_params->ev_pos_innov_gate),
_param_ekf2_of_n_min(_params->flow_noise),
_param_ekf2_of_n_max(_params->flow_noise_qual_min),
_param_ekf2_of_qmin(_params->flow_qual_min),
_param_ekf2_of_gate(_params->flow_innov_gate),
_param_ekf2_imu_pos_x(_params->imu_pos_body(0)),
_param_ekf2_imu_pos_y(_params->imu_pos_body(1)),
_param_ekf2_imu_pos_z(_params->imu_pos_body(2)),
_param_ekf2_gps_pos_x(_params->gps_pos_body(0)),
_param_ekf2_gps_pos_y(_params->gps_pos_body(1)),
_param_ekf2_gps_pos_z(_params->gps_pos_body(2)),
_param_ekf2_rng_pos_x(_params->rng_pos_body(0)),
_param_ekf2_rng_pos_y(_params->rng_pos_body(1)),
_param_ekf2_rng_pos_z(_params->rng_pos_body(2)),
_param_ekf2_of_pos_x(_params->flow_pos_body(0)),
_param_ekf2_of_pos_y(_params->flow_pos_body(1)),
_param_ekf2_of_pos_z(_params->flow_pos_body(2)),
_param_ekf2_ev_pos_x(_params->ev_pos_body(0)),
_param_ekf2_ev_pos_y(_params->ev_pos_body(1)),
_param_ekf2_ev_pos_z(_params->ev_pos_body(2)),
_param_ekf2_tau_vel(_params->vel_Tau),
_param_ekf2_tau_pos(_params->pos_Tau),
_param_ekf2_gbias_init(_params->switch_on_gyro_bias),
_param_ekf2_abias_init(_params->switch_on_accel_bias),
_param_ekf2_angerr_init(_params->initial_tilt_err),
_param_ekf2_abl_lim(_params->acc_bias_lim),
_param_ekf2_abl_acclim(_params->acc_bias_learn_acc_lim),
_param_ekf2_abl_gyrlim(_params->acc_bias_learn_gyr_lim),
_param_ekf2_abl_tau(_params->acc_bias_learn_tc),
_param_ekf2_drag_noise(_params->drag_noise),
_param_ekf2_bcoef_x(_params->bcoef_x),
_param_ekf2_bcoef_y(_params->bcoef_y),
_param_ekf2_aspd_max(_params->max_correction_airspeed),
_param_ekf2_pcoef_xp(_params->static_pressure_coef_xp),
_param_ekf2_pcoef_xn(_params->static_pressure_coef_xn),
_param_ekf2_pcoef_yp(_params->static_pressure_coef_yp),
_param_ekf2_pcoef_yn(_params->static_pressure_coef_yn),
_param_ekf2_pcoef_z(_params->static_pressure_coef_z),
_param_ekf2_move_test(_params->is_moving_scaler),
_param_ekf2_mag_check(_params->check_mag_strength),
_param_ekf2_gsf_tas_default(_params->EKFGSF_tas_default)
{
// initialise parameter cache
updateParams();
_ekf.set_min_required_gps_health_time(_param_ekf2_req_gps_h.get() * 1_s);
}
EKF2::~EKF2()
{
px4_lockstep_unregister_component(_lockstep_component);
perf_free(_ekf_update_perf);
}
bool EKF2::init()
{
const uint32_t device_id = _param_ekf2_imu_id.get();
// if EKF2_IMU_ID is non-zero we use the corresponding IMU, otherwise the voted primary (sensor_combined)
if (device_id != 0) {
for (int i = 0; i < MAX_SENSOR_COUNT; i++) {
vehicle_imu_s imu;
if (_vehicle_imu_sub.ChangeInstance(i) && _vehicle_imu_sub.copy(&imu)) {
if ((imu.accel_device_id > 0) && (imu.accel_device_id == device_id)) {
if (_vehicle_imu_sub.registerCallback()) {
PX4_INFO("subscribed to vehicle_imu:%d (%d)", i, device_id);
_imu_sub_index = i;
_callback_registered = true;
return true;
}
}
}
}
} else {
_imu_sub_index = -1;
if (_sensor_combined_sub.registerCallback()) {
_callback_registered = true;
return true;
}
}
PX4_WARN("failed to register callback, retrying in 1 second");
ScheduleDelayed(1_s); // retry in 1 second
return true;
}
int EKF2::print_status()
{
PX4_INFO("local position: %s", (_ekf.local_position_is_valid()) ? "valid" : "invalid");
PX4_INFO("global position: %s", (_ekf.global_position_is_valid()) ? "valid" : "invalid");
PX4_INFO("time slip: %" PRId64 " us", _last_time_slip_us);
perf_print_counter(_ekf_update_perf);
return 0;
}
template<typename Param>
void EKF2::update_mag_bias(Param &mag_bias_param, int axis_index)
{
if (_valid_cal_available[axis_index]) {
// calculate weighting using ratio of variances and update stored bias values
const float weighting = constrain(_param_ekf2_magb_vref.get() / (_param_ekf2_magb_vref.get() +
_last_valid_variance[axis_index]), 0.0f, _param_ekf2_magb_k.get());
const float mag_bias_saved = mag_bias_param.get();
_last_valid_mag_cal[axis_index] = weighting * _last_valid_mag_cal[axis_index] + mag_bias_saved;
mag_bias_param.set(_last_valid_mag_cal[axis_index]);
mag_bias_param.commit_no_notification();
_valid_cal_available[axis_index] = false;
}
}
template<typename Param>
bool EKF2::update_mag_decl(Param &mag_decl_param)
{
// update stored declination value
float declination_deg;
if (_ekf.get_mag_decl_deg(&declination_deg)) {
mag_decl_param.set(declination_deg);
mag_decl_param.commit_no_notification();
return true;
}
return false;
}
void EKF2::Run()
{
if (should_exit()) {
_sensor_combined_sub.unregisterCallback();
_vehicle_imu_sub.unregisterCallback();
exit_and_cleanup();
return;
}
if (!_callback_registered) {
init();
return;
}
bool updated = false;
imuSample imu_sample_new {};
hrt_abstime imu_dt = 0; // for tracking time slip later
estimator_sensor_bias_s bias{};
if (_imu_sub_index >= 0) {
vehicle_imu_s imu;
updated = _vehicle_imu_sub.update(&imu);
imu_sample_new.time_us = imu.timestamp_sample;
imu_sample_new.delta_ang_dt = imu.delta_angle_dt * 1.e-6f;
imu_sample_new.delta_ang = Vector3f{imu.delta_angle};
imu_sample_new.delta_vel_dt = imu.delta_velocity_dt * 1.e-6f;
imu_sample_new.delta_vel = Vector3f{imu.delta_velocity};
if (imu.delta_velocity_clipping > 0) {
imu_sample_new.delta_vel_clipping[0] = imu.delta_velocity_clipping & vehicle_imu_s::CLIPPING_X;
imu_sample_new.delta_vel_clipping[1] = imu.delta_velocity_clipping & vehicle_imu_s::CLIPPING_Y;
imu_sample_new.delta_vel_clipping[2] = imu.delta_velocity_clipping & vehicle_imu_s::CLIPPING_Z;
}
imu_dt = imu.delta_angle_dt;
bias.accel_device_id = imu.accel_device_id;
bias.gyro_device_id = imu.gyro_device_id;
} else {
sensor_combined_s sensor_combined;
updated = _sensor_combined_sub.update(&sensor_combined);
imu_sample_new.time_us = sensor_combined.timestamp;
imu_sample_new.delta_ang_dt = sensor_combined.gyro_integral_dt * 1.e-6f;
imu_sample_new.delta_ang = Vector3f{sensor_combined.gyro_rad} * imu_sample_new.delta_ang_dt;
imu_sample_new.delta_vel_dt = sensor_combined.accelerometer_integral_dt * 1.e-6f;
imu_sample_new.delta_vel = Vector3f{sensor_combined.accelerometer_m_s2} * imu_sample_new.delta_vel_dt;
if (sensor_combined.accelerometer_clipping > 0) {
imu_sample_new.delta_vel_clipping[0] = sensor_combined.accelerometer_clipping & sensor_combined_s::CLIPPING_X;
imu_sample_new.delta_vel_clipping[1] = sensor_combined.accelerometer_clipping & sensor_combined_s::CLIPPING_Y;
imu_sample_new.delta_vel_clipping[2] = sensor_combined.accelerometer_clipping & sensor_combined_s::CLIPPING_Z;
}
imu_dt = sensor_combined.gyro_integral_dt;
}
if (updated) {
// check for parameter updates
if (_parameter_update_sub.updated()) {
// clear update
parameter_update_s pupdate;
_parameter_update_sub.copy(&pupdate);
// update parameters from storage
updateParams();
}
const hrt_abstime now = imu_sample_new.time_us;
// ekf2_timestamps (using 0.1 ms relative timestamps)
ekf2_timestamps_s ekf2_timestamps{};
ekf2_timestamps.timestamp = now;
ekf2_timestamps.airspeed_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID;
ekf2_timestamps.distance_sensor_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID;
ekf2_timestamps.optical_flow_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID;
ekf2_timestamps.vehicle_air_data_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID;
ekf2_timestamps.vehicle_magnetometer_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID;
ekf2_timestamps.visual_odometry_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID;
// update all other topics if they have new data
if (_status_sub.update(&_vehicle_status)) {
const bool is_fixed_wing = (_vehicle_status.vehicle_type == vehicle_status_s::VEHICLE_TYPE_FIXED_WING);
// only fuse synthetic sideslip measurements if conditions are met
_ekf.set_fuse_beta_flag(is_fixed_wing && (_param_ekf2_fuse_beta.get() == 1));
// let the EKF know if the vehicle motion is that of a fixed wing (forward flight only relative to wind)
_ekf.set_is_fixed_wing(is_fixed_wing);
}
// Always update sensor selction first time through if time stamp is non zero
if (_sensor_selection_sub.updated() || (_sensor_selection.timestamp == 0)) {
const sensor_selection_s sensor_selection_prev = _sensor_selection;
if (_sensor_selection_sub.copy(&_sensor_selection)) {
if ((sensor_selection_prev.timestamp > 0) && (_sensor_selection.timestamp > sensor_selection_prev.timestamp)) {
if (_imu_sub_index < 0) {
if (_sensor_selection.accel_device_id != sensor_selection_prev.accel_device_id) {
_imu_bias_reset_request = true;
}
if (_sensor_selection.gyro_device_id != sensor_selection_prev.gyro_device_id) {
_imu_bias_reset_request = true;
}
}
}
}
}
// attempt reset until successful
if (_imu_bias_reset_request) {
_imu_bias_reset_request = !_ekf.reset_imu_bias();
}
// push imu data into estimator
_ekf.setIMUData(imu_sample_new);
// publish attitude immediately (uses quaternion from output predictor)
publish_attitude(now);
// read mag data
if (_magnetometer_sub.updated()) {
vehicle_magnetometer_s magnetometer;
if (_magnetometer_sub.copy(&magnetometer)) {
// Reset learned bias parameters if there has been a persistant change in magnetometer ID
// Do not reset parmameters when armed to prevent potential time slips casued by parameter set
// and notification events
// Check if there has been a persistant change in magnetometer ID
if (magnetometer.device_id != 0
&& (magnetometer.device_id != (uint32_t)_param_ekf2_magbias_id.get())) {
if (_invalid_mag_id_count < 200) {
_invalid_mag_id_count++;
}
} else {
if (_invalid_mag_id_count > 0) {
_invalid_mag_id_count--;
}
}
if ((_vehicle_status.arming_state != vehicle_status_s::ARMING_STATE_ARMED) && (_invalid_mag_id_count > 100)) {
// the sensor ID used for the last saved mag bias is not confirmed to be the same as the current sensor ID
// this means we need to reset the learned bias values to zero
_param_ekf2_magbias_x.set(0.f);
_param_ekf2_magbias_x.commit_no_notification();
_param_ekf2_magbias_y.set(0.f);
_param_ekf2_magbias_y.commit_no_notification();
_param_ekf2_magbias_z.set(0.f);
_param_ekf2_magbias_z.commit_no_notification();
_param_ekf2_magbias_id.set(magnetometer.device_id);
_param_ekf2_magbias_id.commit();
_invalid_mag_id_count = 0;
PX4_INFO("Mag sensor ID changed to %i", _param_ekf2_magbias_id.get());
}
magSample mag_sample {};
mag_sample.mag(0) = magnetometer.magnetometer_ga[0] - _param_ekf2_magbias_x.get();
mag_sample.mag(1) = magnetometer.magnetometer_ga[1] - _param_ekf2_magbias_y.get();
mag_sample.mag(2) = magnetometer.magnetometer_ga[2] - _param_ekf2_magbias_z.get();
mag_sample.time_us = magnetometer.timestamp;
_ekf.setMagData(mag_sample);
ekf2_timestamps.vehicle_magnetometer_timestamp_rel = (int16_t)((int64_t)magnetometer.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
// read baro data
if (_airdata_sub.updated()) {
vehicle_air_data_s airdata;
if (_airdata_sub.copy(&airdata)) {
_ekf.set_air_density(airdata.rho);
const baroSample baro_sample {airdata.baro_alt_meter, airdata.timestamp_sample};
_ekf.setBaroData(baro_sample);
ekf2_timestamps.vehicle_air_data_timestamp_rel = (int16_t)((int64_t)airdata.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
// read gps1 data if available
bool gps1_updated = _gps_subs[0].updated();
if (gps1_updated) {
vehicle_gps_position_s gps;
if (_gps_subs[0].copy(&gps)) {
fillGpsMsgWithVehicleGpsPosData(_gps_state[0], gps);
_gps_alttitude_ellipsoid[0] = gps.alt_ellipsoid;
}
}
// check for second GPS receiver data
bool gps2_updated = _gps_subs[1].updated();
if (gps2_updated) {
vehicle_gps_position_s gps;
if (_gps_subs[1].copy(&gps)) {
fillGpsMsgWithVehicleGpsPosData(_gps_state[1], gps);
_gps_alttitude_ellipsoid[1] = gps.alt_ellipsoid;
}
}
if ((_param_ekf2_gps_mask.get() == 0) && gps1_updated) {
// When GPS blending is disabled we always use the first receiver instance
_ekf.setGpsData(_gps_state[0]);
} else if ((_param_ekf2_gps_mask.get() > 0) && (gps1_updated || gps2_updated)) {
// blend dual receivers if available
// calculate blending weights
if (!blend_gps_data()) {
// handle case where the blended states cannot be updated
// Only use selected receiver data if it has been updated
_gps_new_output_data = (gps1_updated && _gps_select_index == 0) ||
(gps2_updated && _gps_select_index == 1);
// Reset relative position offsets to zero
_NE_pos_offset_m[0].zero();
_NE_pos_offset_m[1].zero();
_hgt_offset_mm[0] = _hgt_offset_mm[1] = 0.0f;
}
if (_gps_new_output_data) {
// correct the _gps_state data for steady state offsets and write to _gps_output
apply_gps_offsets();
// calculate a blended output from the offset corrected receiver data
if (_gps_select_index == 2) {
calc_gps_blend_output();
}
// write selected GPS to EKF
_ekf.setGpsData(_gps_output[_gps_select_index]);
// log blended solution as a third GPS instance
ekf_gps_position_s gps;
gps.timestamp = _gps_output[_gps_select_index].time_usec;
gps.lat = _gps_output[_gps_select_index].lat;
gps.lon = _gps_output[_gps_select_index].lon;
gps.alt = _gps_output[_gps_select_index].alt;
gps.fix_type = _gps_output[_gps_select_index].fix_type;
gps.eph = _gps_output[_gps_select_index].eph;
gps.epv = _gps_output[_gps_select_index].epv;
gps.s_variance_m_s = _gps_output[_gps_select_index].sacc;
gps.vel_m_s = _gps_output[_gps_select_index].vel_m_s;
gps.vel_n_m_s = _gps_output[_gps_select_index].vel_ned(0);
gps.vel_e_m_s = _gps_output[_gps_select_index].vel_ned(1);
gps.vel_d_m_s = _gps_output[_gps_select_index].vel_ned(2);
gps.vel_ned_valid = _gps_output[_gps_select_index].vel_ned_valid;
gps.satellites_used = _gps_output[_gps_select_index].nsats;
gps.heading = _gps_output[_gps_select_index].yaw;
gps.heading_offset = _gps_output[_gps_select_index].yaw_offset;
gps.selected = _gps_select_index;
// Publish to the EKF blended GPS topic
_blended_gps_pub.publish(gps);
// clear flag to avoid re-use of the same data
_gps_new_output_data = false;
}
}
if (_airspeed_sub.updated()) {
airspeed_s airspeed;
if (_airspeed_sub.copy(&airspeed)) {
// only set airspeed data if condition for airspeed fusion are met
if ((_param_ekf2_arsp_thr.get() > FLT_EPSILON) && (airspeed.true_airspeed_m_s > _param_ekf2_arsp_thr.get())) {
airspeedSample airspeed_sample {};
airspeed_sample.time_us = airspeed.timestamp;
airspeed_sample.eas2tas = airspeed.true_airspeed_m_s / airspeed.indicated_airspeed_m_s;
airspeed_sample.true_airspeed = airspeed.true_airspeed_m_s;
_ekf.setAirspeedData(airspeed_sample);
}
ekf2_timestamps.airspeed_timestamp_rel = (int16_t)((int64_t)airspeed.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
if (_optical_flow_sub.updated()) {
optical_flow_s optical_flow;
if (_optical_flow_sub.copy(&optical_flow)) {
flowSample flow {};
// NOTE: the EKF uses the reverse sign convention to the flow sensor. EKF assumes positive LOS rate
// is produced by a RH rotation of the image about the sensor axis.
flow.flow_xy_rad(0) = -optical_flow.pixel_flow_x_integral;
flow.flow_xy_rad(1) = -optical_flow.pixel_flow_y_integral;
flow.gyro_xyz(0) = -optical_flow.gyro_x_rate_integral;
flow.gyro_xyz(1) = -optical_flow.gyro_y_rate_integral;
flow.gyro_xyz(2) = -optical_flow.gyro_z_rate_integral;
flow.quality = optical_flow.quality;
flow.dt = 1e-6f * (float)optical_flow.integration_timespan;
flow.time_us = optical_flow.timestamp;
if (PX4_ISFINITE(optical_flow.pixel_flow_y_integral) &&
PX4_ISFINITE(optical_flow.pixel_flow_x_integral) &&
flow.dt < 1) {
_ekf.setOpticalFlowData(flow);
}
// Save sensor limits reported by the optical flow sensor
_ekf.set_optical_flow_limits(optical_flow.max_flow_rate, optical_flow.min_ground_distance,
optical_flow.max_ground_distance);
ekf2_timestamps.optical_flow_timestamp_rel = (int16_t)((int64_t)optical_flow.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
if (_range_finder_sub_index >= 0) {
if (_distance_sensor_subs[_range_finder_sub_index].updated()) {
distance_sensor_s range_finder;
if (_distance_sensor_subs[_range_finder_sub_index].copy(&range_finder)) {
rangeSample range_sample {};
range_sample.rng = range_finder.current_distance;
range_sample.quality = range_finder.signal_quality;
range_sample.time_us = range_finder.timestamp;
_ekf.setRangeData(range_sample);
// Save sensor limits reported by the rangefinder
_ekf.set_rangefinder_limits(range_finder.min_distance, range_finder.max_distance);
ekf2_timestamps.distance_sensor_timestamp_rel = (int16_t)((int64_t)range_finder.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
} else {
_range_finder_sub_index = getRangeSubIndex();
}
// get external vision data
// if error estimates are unavailable, use parameter defined defaults
new_ev_data_received = false;
if (_ev_odom_sub.updated()) {
new_ev_data_received = true;
// copy both attitude & position, we need both to fill a single extVisionSample
_ev_odom_sub.copy(&_ev_odom);
extVisionSample ev_data {};
// check for valid velocity data
if (PX4_ISFINITE(_ev_odom.vx) && PX4_ISFINITE(_ev_odom.vy) && PX4_ISFINITE(_ev_odom.vz)) {
ev_data.vel(0) = _ev_odom.vx;
ev_data.vel(1) = _ev_odom.vy;
ev_data.vel(2) = _ev_odom.vz;
if (_ev_odom.velocity_frame == vehicle_odometry_s::BODY_FRAME_FRD) {
ev_data.vel_frame = estimator::BODY_FRAME_FRD;
} else {
ev_data.vel_frame = estimator::LOCAL_FRAME_FRD;
}
// velocity measurement error from ev_data or parameters
float param_evv_noise_var = sq(_param_ekf2_evv_noise.get());
if (!_param_ekf2_ev_noise_md.get() && PX4_ISFINITE(_ev_odom.velocity_covariance[_ev_odom.COVARIANCE_MATRIX_VX_VARIANCE])
&& PX4_ISFINITE(_ev_odom.velocity_covariance[_ev_odom.COVARIANCE_MATRIX_VY_VARIANCE])
&& PX4_ISFINITE(_ev_odom.velocity_covariance[_ev_odom.COVARIANCE_MATRIX_VZ_VARIANCE])) {
ev_data.velCov(0, 0) = _ev_odom.velocity_covariance[_ev_odom.COVARIANCE_MATRIX_VX_VARIANCE];
ev_data.velCov(0, 1) = ev_data.velCov(1, 0) = _ev_odom.velocity_covariance[1];
ev_data.velCov(0, 2) = ev_data.velCov(2, 0) = _ev_odom.velocity_covariance[2];
ev_data.velCov(1, 1) = _ev_odom.velocity_covariance[_ev_odom.COVARIANCE_MATRIX_VY_VARIANCE];
ev_data.velCov(1, 2) = ev_data.velCov(2, 1) = _ev_odom.velocity_covariance[7];
ev_data.velCov(2, 2) = _ev_odom.velocity_covariance[_ev_odom.COVARIANCE_MATRIX_VZ_VARIANCE];
} else {
ev_data.velCov = matrix::eye<float, 3>() * param_evv_noise_var;
}
}
// check for valid position data
if (PX4_ISFINITE(_ev_odom.x) && PX4_ISFINITE(_ev_odom.y) && PX4_ISFINITE(_ev_odom.z)) {
ev_data.pos(0) = _ev_odom.x;
ev_data.pos(1) = _ev_odom.y;
ev_data.pos(2) = _ev_odom.z;
float param_evp_noise_var = sq(_param_ekf2_evp_noise.get());
// position measurement error from ev_data or parameters
if (!_param_ekf2_ev_noise_md.get() && PX4_ISFINITE(_ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_X_VARIANCE])
&& PX4_ISFINITE(_ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_Y_VARIANCE])
&& PX4_ISFINITE(_ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_Z_VARIANCE])) {
ev_data.posVar(0) = fmaxf(param_evp_noise_var, _ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_X_VARIANCE]);
ev_data.posVar(1) = fmaxf(param_evp_noise_var, _ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_Y_VARIANCE]);
ev_data.posVar(2) = fmaxf(param_evp_noise_var, _ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_Z_VARIANCE]);
} else {
ev_data.posVar.setAll(param_evp_noise_var);
}
}
// check for valid orientation data
if (PX4_ISFINITE(_ev_odom.q[0])) {
ev_data.quat = matrix::Quatf(_ev_odom.q);
// orientation measurement error from ev_data or parameters
float param_eva_noise_var = sq(_param_ekf2_eva_noise.get());
if (!_param_ekf2_ev_noise_md.get() && PX4_ISFINITE(_ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_YAW_VARIANCE])) {
ev_data.angVar = fmaxf(param_eva_noise_var, _ev_odom.pose_covariance[_ev_odom.COVARIANCE_MATRIX_YAW_VARIANCE]);
} else {
ev_data.angVar = param_eva_noise_var;
}
}
// use timestamp from external computer, clocks are synchronized when using MAVROS
ev_data.time_us = _ev_odom.timestamp_sample;
_ekf.setExtVisionData(ev_data);
ekf2_timestamps.visual_odometry_timestamp_rel = (int16_t)((int64_t)_ev_odom.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
bool vehicle_land_detected_updated = _vehicle_land_detected_sub.updated();
if (vehicle_land_detected_updated) {
if (_vehicle_land_detected_sub.copy(&_vehicle_land_detected)) {
_ekf.set_in_air_status(!_vehicle_land_detected.landed);
}
}
// use the landing target pose estimate as another source of velocity data
if (_landing_target_pose_sub.updated()) {
landing_target_pose_s landing_target_pose;
if (_landing_target_pose_sub.copy(&landing_target_pose)) {
// we can only use the landing target if it has a fixed position and a valid velocity estimate
if (landing_target_pose.is_static && landing_target_pose.rel_vel_valid) {
// velocity of vehicle relative to target has opposite sign to target relative to vehicle
auxVelSample auxvel_sample {};
auxvel_sample.vel = matrix::Vector3f{-landing_target_pose.vx_rel, -landing_target_pose.vy_rel, 0.0f};
auxvel_sample.velVar = matrix::Vector3f{landing_target_pose.cov_vx_rel, landing_target_pose.cov_vy_rel, 0.0f};
auxvel_sample.time_us = landing_target_pose.timestamp;
_ekf.setAuxVelData(auxvel_sample);
}
}
}
// run the EKF update and output
perf_begin(_ekf_update_perf);
const bool ekf_updated = _ekf.update();
perf_end(_ekf_update_perf);
// integrate time to monitor time slippage
if (_start_time_us == 0) {
_start_time_us = now;
_last_time_slip_us = 0;
} else if (_start_time_us > 0) {
_integrated_time_us += imu_dt;
_last_time_slip_us = (now - _start_time_us) - _integrated_time_us;
}
if (ekf_updated) {
filter_control_status_u control_status;
_ekf.get_control_mode(&control_status.value);
// only publish position after successful alignment
if (control_status.flags.tilt_align) {
// generate vehicle local position data
vehicle_local_position_s &lpos = _vehicle_local_position_pub.get();
// generate vehicle odometry data
vehicle_odometry_s odom{};
lpos.timestamp = now;
odom.timestamp = hrt_absolute_time();
odom.timestamp_sample = now;
odom.local_frame = vehicle_odometry_s::LOCAL_FRAME_NED;
// Position of body origin in local NED frame
Vector3f position = _ekf.getPosition();
const float lpos_x_prev = lpos.x;
const float lpos_y_prev = lpos.y;
lpos.x = (_ekf.local_position_is_valid()) ? position(0) : 0.0f;
lpos.y = (_ekf.local_position_is_valid()) ? position(1) : 0.0f;
lpos.z = position(2);
// Vehicle odometry position
odom.x = lpos.x;
odom.y = lpos.y;
odom.z = lpos.z;
// Velocity of body origin in local NED frame (m/s)
const Vector3f velocity = _ekf.getVelocity();
lpos.vx = velocity(0);
lpos.vy = velocity(1);
lpos.vz = velocity(2);
// Vehicle odometry linear velocity
odom.velocity_frame = vehicle_odometry_s::LOCAL_FRAME_FRD;
odom.vx = lpos.vx;
odom.vy = lpos.vy;
odom.vz = lpos.vz;
// vertical position time derivative (m/s)
lpos.z_deriv = _ekf.getVerticalPositionDerivative();
// Acceleration of body origin in local frame
Vector3f vel_deriv = _ekf.getVelocityDerivative();
lpos.ax = vel_deriv(0);
lpos.ay = vel_deriv(1);
lpos.az = vel_deriv(2);
// TODO: better status reporting
lpos.xy_valid = _ekf.local_position_is_valid() && !_preflt_checker.hasHorizFailed();
lpos.z_valid = !_preflt_checker.hasVertFailed();
lpos.v_xy_valid = _ekf.local_position_is_valid() && !_preflt_checker.hasHorizFailed();
lpos.v_z_valid = !_preflt_checker.hasVertFailed();
// Position of local NED origin in GPS / WGS84 frame
map_projection_reference_s ekf_origin;
uint64_t origin_time;
// true if position (x,y,z) has a valid WGS-84 global reference (ref_lat, ref_lon, alt)
const bool ekf_origin_valid = _ekf.get_ekf_origin(&origin_time, &ekf_origin, &lpos.ref_alt);
lpos.xy_global = ekf_origin_valid;
lpos.z_global = ekf_origin_valid;
if (ekf_origin_valid && (origin_time > lpos.ref_timestamp)) {
lpos.ref_timestamp = origin_time;
lpos.ref_lat = ekf_origin.lat_rad * 180.0 / M_PI; // Reference point latitude in degrees
lpos.ref_lon = ekf_origin.lon_rad * 180.0 / M_PI; // Reference point longitude in degrees
}
// The rotation of the tangent plane vs. geographical north
const matrix::Quatf q = _ekf.getQuaternion();
matrix::Quatf delta_q_reset;
_ekf.get_quat_reset(&delta_q_reset(0), &lpos.heading_reset_counter);
lpos.heading = matrix::Eulerf(q).psi();
lpos.delta_heading = matrix::Eulerf(delta_q_reset).psi();
// Vehicle odometry quaternion
q.copyTo(odom.q);
// Vehicle odometry angular rates
const Vector3f gyro_bias = _ekf.getGyroBias();
const Vector3f rates(imu_sample_new.delta_ang * imu_sample_new.delta_ang_dt);
odom.rollspeed = rates(0) - gyro_bias(0);
odom.pitchspeed = rates(1) - gyro_bias(1);
odom.yawspeed = rates(2) - gyro_bias(2);
lpos.dist_bottom_valid = _ekf.get_terrain_valid();
float terrain_vpos = _ekf.getTerrainVertPos();
lpos.dist_bottom = terrain_vpos - lpos.z; // Distance to bottom surface (ground) in meters
// constrain the distance to ground to _rng_gnd_clearance
if (lpos.dist_bottom < _param_ekf2_min_rng.get()) {
lpos.dist_bottom = _param_ekf2_min_rng.get();
}
if (!_had_valid_terrain) {
_had_valid_terrain = lpos.dist_bottom_valid;
}
// only consider ground effect if compensation is configured and the vehicle is armed (props spinning)
if (_param_ekf2_gnd_eff_dz.get() > 0.0f && (_vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_ARMED)) {
// set ground effect flag if vehicle is closer than a specified distance to the ground
if (lpos.dist_bottom_valid) {
_ekf.set_gnd_effect_flag(lpos.dist_bottom < _param_ekf2_gnd_max_hgt.get());
// if we have no valid terrain estimate and never had one then use ground effect flag from land detector
// _had_valid_terrain is used to make sure that we don't fall back to using this option
// if we temporarily lose terrain data due to the distance sensor getting out of range
} else if (vehicle_land_detected_updated && !_had_valid_terrain) {
// update ground effect flag based on land detector state
_ekf.set_gnd_effect_flag(_vehicle_land_detected.in_ground_effect);
}
} else {
_ekf.set_gnd_effect_flag(false);
}
_ekf.get_ekf_lpos_accuracy(&lpos.eph, &lpos.epv);
_ekf.get_ekf_vel_accuracy(&lpos.evh, &lpos.evv);
// get state reset information of position and velocity
_ekf.get_posD_reset(&lpos.delta_z, &lpos.z_reset_counter);
_ekf.get_velD_reset(&lpos.delta_vz, &lpos.vz_reset_counter);
_ekf.get_posNE_reset(&lpos.delta_xy[0], &lpos.xy_reset_counter);
_ekf.get_velNE_reset(&lpos.delta_vxy[0], &lpos.vxy_reset_counter);
// get control limit information
_ekf.get_ekf_ctrl_limits(&lpos.vxy_max, &lpos.vz_max, &lpos.hagl_min, &lpos.hagl_max);
// convert NaN to INFINITY
if (!PX4_ISFINITE(lpos.vxy_max)) {
lpos.vxy_max = INFINITY;
}
if (!PX4_ISFINITE(lpos.vz_max)) {
lpos.vz_max = INFINITY;
}
if (!PX4_ISFINITE(lpos.hagl_min)) {
lpos.hagl_min = INFINITY;
}
if (!PX4_ISFINITE(lpos.hagl_max)) {
lpos.hagl_max = INFINITY;
}
// Get covariances to vehicle odometry
float covariances[24];
_ekf.covariances_diagonal().copyTo(covariances);
// get the covariance matrix size
const size_t POS_URT_SIZE = sizeof(odom.pose_covariance) / sizeof(odom.pose_covariance[0]);
const size_t VEL_URT_SIZE = sizeof(odom.velocity_covariance) / sizeof(odom.velocity_covariance[0]);
// initially set pose covariances to 0
for (size_t i = 0; i < POS_URT_SIZE; i++) {
odom.pose_covariance[i] = 0.0;
}
// set the position variances
odom.pose_covariance[odom.COVARIANCE_MATRIX_X_VARIANCE] = covariances[7];
odom.pose_covariance[odom.COVARIANCE_MATRIX_Y_VARIANCE] = covariances[8];
odom.pose_covariance[odom.COVARIANCE_MATRIX_Z_VARIANCE] = covariances[9];
// TODO: implement propagation from quaternion covariance to Euler angle covariance
// by employing the covariance law
// initially set velocity covariances to 0
for (size_t i = 0; i < VEL_URT_SIZE; i++) {
odom.velocity_covariance[i] = 0.0;
}
// set the linear velocity variances
odom.velocity_covariance[odom.COVARIANCE_MATRIX_VX_VARIANCE] = covariances[4];
odom.velocity_covariance[odom.COVARIANCE_MATRIX_VY_VARIANCE] = covariances[5];
odom.velocity_covariance[odom.COVARIANCE_MATRIX_VZ_VARIANCE] = covariances[6];
// publish vehicle local position data
_vehicle_local_position_pub.update();
// publish vehicle odometry data
_vehicle_odometry_pub.publish(odom);
// publish external visual odometry after fixed frame alignment if new odometry is received
if (new_ev_data_received) {
const Quatf quat_ev2ekf = _ekf.getVisionAlignmentQuaternion(); // rotates from EV to EKF navigation frame
const Dcmf ev_rot_mat(quat_ev2ekf);
vehicle_odometry_s aligned_ev_odom = _ev_odom;
// Rotate external position and velocity into EKF navigation frame
const Vector3f aligned_pos = ev_rot_mat * Vector3f(_ev_odom.x, _ev_odom.y, _ev_odom.z);
aligned_ev_odom.x = aligned_pos(0);
aligned_ev_odom.y = aligned_pos(1);
aligned_ev_odom.z = aligned_pos(2);
switch (_ev_odom.velocity_frame) {
case vehicle_odometry_s::BODY_FRAME_FRD: {
const Vector3f aligned_vel = Dcmf(_ekf.getQuaternion()) *
Vector3f(_ev_odom.vx, _ev_odom.vy, _ev_odom.vz);
aligned_ev_odom.vx = aligned_vel(0);
aligned_ev_odom.vy = aligned_vel(1);
aligned_ev_odom.vz = aligned_vel(2);
break;
}
case vehicle_odometry_s::LOCAL_FRAME_FRD: {
const Vector3f aligned_vel = ev_rot_mat *
Vector3f(_ev_odom.vx, _ev_odom.vy, _ev_odom.vz);
aligned_ev_odom.vx = aligned_vel(0);
aligned_ev_odom.vy = aligned_vel(1);
aligned_ev_odom.vz = aligned_vel(2);
break;
}
}
aligned_ev_odom.velocity_frame = vehicle_odometry_s::LOCAL_FRAME_NED;
// Compute orientation in EKF navigation frame
Quatf ev_quat_aligned = quat_ev2ekf * matrix::Quatf(_ev_odom.q) ;
ev_quat_aligned.normalize();
ev_quat_aligned.copyTo(aligned_ev_odom.q);
quat_ev2ekf.copyTo(aligned_ev_odom.q_offset);
_vehicle_visual_odometry_aligned_pub.publish(aligned_ev_odom);
}
if (_ekf.global_position_is_valid() && !_preflt_checker.hasFailed()) {
// generate and publish global position data
vehicle_global_position_s &global_pos = _vehicle_global_position_pub.get();
global_pos.timestamp = now;
if (fabsf(lpos_x_prev - lpos.x) > FLT_EPSILON || fabsf(lpos_y_prev - lpos.y) > FLT_EPSILON) {
map_projection_reproject(&ekf_origin, lpos.x, lpos.y, &global_pos.lat, &global_pos.lon);
}
global_pos.lat_lon_reset_counter = lpos.xy_reset_counter;
global_pos.alt = -lpos.z + lpos.ref_alt; // Altitude AMSL in meters
global_pos.alt_ellipsoid = filter_altitude_ellipsoid(global_pos.alt);
// global altitude has opposite sign of local down position
global_pos.delta_alt = -lpos.delta_z;
_ekf.get_ekf_gpos_accuracy(&global_pos.eph, &global_pos.epv);
global_pos.terrain_alt_valid = lpos.dist_bottom_valid;
if (global_pos.terrain_alt_valid) {
global_pos.terrain_alt = lpos.ref_alt - terrain_vpos; // Terrain altitude in m, WGS84
} else {
global_pos.terrain_alt = 0.0f; // Terrain altitude in m, WGS84
}
global_pos.dead_reckoning = _ekf.inertial_dead_reckoning(); // True if this position is estimated through dead-reckoning
_vehicle_global_position_pub.update();
}
}
// publish estimator states
estimator_states_s states;
states.n_states = 24;
_ekf.getStateAtFusionHorizonAsVector().copyTo(states.states);
_ekf.covariances_diagonal().copyTo(states.covariances);
states.timestamp = _replay_mode ? now : hrt_absolute_time();
_estimator_states_pub.publish(states);
// publish estimator status
estimator_status_s status{};
_ekf.getOutputTrackingError().copyTo(status.output_tracking_error);
_ekf.get_gps_check_status(&status.gps_check_fail_flags);
// only report enabled GPS check failures (the param indexes are shifted by 1 bit, because they don't include
// the GPS Fix bit, which is always checked)
status.gps_check_fail_flags &= ((uint16_t)_params->gps_check_mask << 1) | 1;
status.control_mode_flags = control_status.value;
_ekf.get_filter_fault_status(&status.filter_fault_flags);
_ekf.get_innovation_test_status(status.innovation_check_flags, status.mag_test_ratio,
status.vel_test_ratio, status.pos_test_ratio,
status.hgt_test_ratio, status.tas_test_ratio,
status.hagl_test_ratio, status.beta_test_ratio);
status.pos_horiz_accuracy = _vehicle_local_position_pub.get().eph;
status.pos_vert_accuracy = _vehicle_local_position_pub.get().epv;
_ekf.get_ekf_soln_status(&status.solution_status_flags);
_ekf.getImuVibrationMetrics().copyTo(status.vibe);
status.time_slip = _last_time_slip_us * 1e-6f;
status.pre_flt_fail_innov_heading = _preflt_checker.hasHeadingFailed();
status.pre_flt_fail_innov_vel_horiz = _preflt_checker.hasHorizVelFailed();
status.pre_flt_fail_innov_vel_vert = _preflt_checker.hasVertVelFailed();
status.pre_flt_fail_innov_height = _preflt_checker.hasHeightFailed();
status.pre_flt_fail_mag_field_disturbed = control_status.flags.mag_field_disturbed;
_estimator_status_pub.publish(status);
{
// publish all corrected sensor readings and bias estimates after mag calibration is updated above
bias.timestamp = now;
// take device ids from sensor_selection_s if not using specific vehicle_imu_s
if (_imu_sub_index < 0) {
bias.gyro_device_id = _sensor_selection.gyro_device_id;
bias.accel_device_id = _sensor_selection.accel_device_id;
}
bias.mag_device_id = _param_ekf2_magbias_id.get();
// In-run bias estimates
_ekf.getGyroBias().copyTo(bias.gyro_bias);
_ekf.getAccelBias().copyTo(bias.accel_bias);
bias.mag_bias[0] = _last_valid_mag_cal[0];
bias.mag_bias[1] = _last_valid_mag_cal[1];
bias.mag_bias[2] = _last_valid_mag_cal[2];
_estimator_sensor_bias_pub.publish(bias);
}
// publish GPS drift data only when updated to minimise overhead
float gps_drift[3];
bool blocked;
if (_ekf.get_gps_drift_metrics(gps_drift, &blocked)) {
ekf_gps_drift_s drift_data;
drift_data.timestamp = now;
drift_data.hpos_drift_rate = gps_drift[0];
drift_data.vpos_drift_rate = gps_drift[1];
drift_data.hspd = gps_drift[2];
drift_data.blocked = blocked;
_ekf_gps_drift_pub.publish(drift_data);
}
{
/* Check and save learned magnetometer bias estimates */
// Check if conditions are OK for learning of magnetometer bias values
if (!_vehicle_land_detected.landed && // not on ground
(_vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_ARMED) && // vehicle is armed
!status.filter_fault_flags && // there are no filter faults
control_status.flags.mag_3D) { // the EKF is operating in the correct mode
if (_last_magcal_us == 0) {
_last_magcal_us = now;
} else {
_total_cal_time_us += now - _last_magcal_us;
_last_magcal_us = now;
}
} else if (status.filter_fault_flags != 0) {
// if a filter fault has occurred, assume previous learning was invalid and do not
// count it towards total learning time.
_total_cal_time_us = 0;
for (bool &cal_available : _valid_cal_available) {
cal_available = false;
}
} else {
// conditions are NOT OK for learning magnetometer bias, reset timestamp
// but keep the accumulated calibration time
_last_magcal_us = now;
}
// Start checking mag bias estimates when we have accumulated sufficient calibration time
if (_total_cal_time_us > 120_s) {
// we have sufficient accumulated valid flight time to form a reliable bias estimate
// check that the state variance for each axis is within a range indicating filter convergence
const float max_var_allowed = 100.0f * _param_ekf2_magb_vref.get();
const float min_var_allowed = 0.01f * _param_ekf2_magb_vref.get();
// Declare all bias estimates invalid if any variances are out of range
bool all_estimates_invalid = false;
for (uint8_t axis_index = 0; axis_index <= 2; axis_index++) {
if (states.covariances[axis_index + 19] < min_var_allowed
|| states.covariances[axis_index + 19] > max_var_allowed) {
all_estimates_invalid = true;
}
}
// Store valid estimates and their associated variances
if (!all_estimates_invalid) {
for (uint8_t axis_index = 0; axis_index <= 2; axis_index++) {
_last_valid_mag_cal[axis_index] = states.states[axis_index + 19];
_valid_cal_available[axis_index] = true;
_last_valid_variance[axis_index] = states.covariances[axis_index + 19];
}
}
}
// Check and save the last valid calibration when we are disarmed
if ((_vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_STANDBY)
&& (status.filter_fault_flags == 0)) {
update_mag_bias(_param_ekf2_magbias_x, 0);
update_mag_bias(_param_ekf2_magbias_y, 1);
update_mag_bias(_param_ekf2_magbias_z, 2);
// reset to prevent data being saved too frequently
_total_cal_time_us = 0;
}
}
publish_wind_estimate(now);
publish_yaw_estimator_status(now);
if (!_mag_decl_saved && (_vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_STANDBY)) {
_mag_decl_saved = update_mag_decl(_param_ekf2_mag_decl);
}
{
// publish estimator innovation data
estimator_innovations_s innovations;
innovations.timestamp = now;
_ekf.getGpsVelPosInnov(&innovations.gps_hvel[0], innovations.gps_vvel, &innovations.gps_hpos[0],
innovations.gps_vpos);
_ekf.getEvVelPosInnov(&innovations.ev_hvel[0], innovations.ev_vvel, &innovations.ev_hpos[0], innovations.ev_vpos);
_ekf.getBaroHgtInnov(innovations.baro_vpos);
_ekf.getRngHgtInnov(innovations.rng_vpos);
_ekf.getAuxVelInnov(&innovations.aux_hvel[0]);
_ekf.getFlowInnov(&innovations.flow[0]);
_ekf.getHeadingInnov(innovations.heading);
_ekf.getMagInnov(innovations.mag_field);
_ekf.getDragInnov(&innovations.drag[0]);
_ekf.getAirspeedInnov(innovations.airspeed);
_ekf.getBetaInnov(innovations.beta);
_ekf.getHaglInnov(innovations.hagl);
// Not yet supported
innovations.aux_vvel = NAN;
innovations.fake_hpos[0] = innovations.fake_hpos[1] = innovations.fake_vpos = NAN;
innovations.fake_hvel[0] = innovations.fake_hvel[1] = innovations.fake_vvel = NAN;
// publish estimator innovation variance data
estimator_innovations_s innovation_var;
innovation_var.timestamp = now;
_ekf.getGpsVelPosInnovVar(&innovation_var.gps_hvel[0], innovation_var.gps_vvel, &innovation_var.gps_hpos[0],
innovation_var.gps_vpos);
_ekf.getEvVelPosInnovVar(&innovation_var.ev_hvel[0], innovation_var.ev_vvel, &innovation_var.ev_hpos[0],
innovation_var.ev_vpos);
_ekf.getBaroHgtInnovVar(innovation_var.baro_vpos);
_ekf.getRngHgtInnovVar(innovation_var.rng_vpos);
_ekf.getAuxVelInnovVar(&innovation_var.aux_hvel[0]);
_ekf.getFlowInnovVar(&innovation_var.flow[0]);
_ekf.getHeadingInnovVar(innovation_var.heading);
_ekf.getMagInnovVar(&innovation_var.mag_field[0]);
_ekf.getDragInnovVar(&innovation_var.drag[0]);
_ekf.getAirspeedInnovVar(innovation_var.airspeed);
_ekf.getBetaInnovVar(innovation_var.beta);
_ekf.getHaglInnovVar(innovation_var.hagl);
// Not yet supported
innovation_var.aux_vvel = NAN;
innovation_var.fake_hpos[0] = innovation_var.fake_hpos[1] = innovation_var.fake_vpos = NAN;
innovation_var.fake_hvel[0] = innovation_var.fake_hvel[1] = innovation_var.fake_vvel = NAN;
// publish estimator innovation test ratio data
estimator_innovations_s test_ratios;
test_ratios.timestamp = now;
_ekf.getGpsVelPosInnovRatio(test_ratios.gps_hvel[0], test_ratios.gps_vvel, test_ratios.gps_hpos[0],
test_ratios.gps_vpos);
_ekf.getEvVelPosInnovRatio(test_ratios.ev_hvel[0], test_ratios.ev_vvel, test_ratios.ev_hpos[0],
test_ratios.ev_vpos);
_ekf.getBaroHgtInnovRatio(test_ratios.baro_vpos);
_ekf.getRngHgtInnovRatio(test_ratios.rng_vpos);
_ekf.getAuxVelInnovRatio(test_ratios.aux_hvel[0]);
_ekf.getFlowInnovRatio(test_ratios.flow[0]);
_ekf.getHeadingInnovRatio(test_ratios.heading);
_ekf.getMagInnovRatio(test_ratios.mag_field[0]);
_ekf.getDragInnovRatio(&test_ratios.drag[0]);
_ekf.getAirspeedInnovRatio(test_ratios.airspeed);
_ekf.getBetaInnovRatio(test_ratios.beta);
_ekf.getHaglInnovRatio(test_ratios.hagl);
// Not yet supported
test_ratios.aux_vvel = NAN;
test_ratios.fake_hpos[0] = test_ratios.fake_hpos[1] = test_ratios.fake_vpos = NAN;
test_ratios.fake_hvel[0] = test_ratios.fake_hvel[1] = test_ratios.fake_vvel = NAN;
// calculate noise filtered velocity innovations which are used for pre-flight checking
if (_vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_STANDBY) {
float dt_seconds = imu_sample_new.delta_ang_dt;
runPreFlightChecks(dt_seconds, control_status, _vehicle_status, innovations);
} else {
resetPreFlightChecks();
}
_estimator_innovations_pub.publish(innovations);
_estimator_innovation_variances_pub.publish(innovation_var);
_estimator_innovation_test_ratios_pub.publish(test_ratios);
}
}
// publish ekf2_timestamps
_ekf2_timestamps_pub.publish(ekf2_timestamps);
if (_lockstep_component == -1) {
_lockstep_component = px4_lockstep_register_component();
}
px4_lockstep_progress(_lockstep_component);
}
}
void EKF2::fillGpsMsgWithVehicleGpsPosData(gps_message &msg, const vehicle_gps_position_s &data)
{
msg.time_usec = data.timestamp;
msg.lat = data.lat;
msg.lon = data.lon;
msg.alt = data.alt;
msg.yaw = data.heading;
msg.yaw_offset = data.heading_offset;
msg.fix_type = data.fix_type;
msg.eph = data.eph;
msg.epv = data.epv;
msg.sacc = data.s_variance_m_s;
msg.vel_m_s = data.vel_m_s;
msg.vel_ned(0) = data.vel_n_m_s;
msg.vel_ned(1) = data.vel_e_m_s;
msg.vel_ned(2) = data.vel_d_m_s;
msg.vel_ned_valid = data.vel_ned_valid;
msg.nsats = data.satellites_used;
msg.pdop = sqrtf(data.hdop * data.hdop + data.vdop * data.vdop);
}
void EKF2::runPreFlightChecks(const float dt,
const filter_control_status_u &control_status,
const vehicle_status_s &vehicle_status,
const estimator_innovations_s &innov)
{
const bool can_observe_heading_in_flight = (vehicle_status.vehicle_type != vehicle_status_s::VEHICLE_TYPE_ROTARY_WING);
_preflt_checker.setVehicleCanObserveHeadingInFlight(can_observe_heading_in_flight);
_preflt_checker.setUsingGpsAiding(control_status.flags.gps);
_preflt_checker.setUsingFlowAiding(control_status.flags.opt_flow);
_preflt_checker.setUsingEvPosAiding(control_status.flags.ev_pos);
_preflt_checker.setUsingEvVelAiding(control_status.flags.ev_vel);
_preflt_checker.update(dt, innov);
}
void EKF2::resetPreFlightChecks()
{
_preflt_checker.reset();
}
int EKF2::getRangeSubIndex()
{
for (unsigned i = 0; i < _distance_sensor_subs.size(); i++) {
distance_sensor_s report;
if (_distance_sensor_subs[i].update(&report)) {
// only use the first instace which has the correct orientation
if (report.orientation == distance_sensor_s::ROTATION_DOWNWARD_FACING) {
PX4_INFO("Found range finder with instance %d", i);
return i;
}
}
}
return -1;
}
void EKF2::publish_attitude(const hrt_abstime &timestamp)
{
if (_ekf.attitude_valid()) {
// generate vehicle attitude quaternion data
vehicle_attitude_s att;
att.timestamp = timestamp;
const Quatf q{_ekf.calculate_quaternion()};
q.copyTo(att.q);
_ekf.get_quat_reset(&att.delta_q_reset[0], &att.quat_reset_counter);
_att_pub.publish(att);
} else if (_replay_mode) {
// in replay mode we have to tell the replay module not to wait for an update
// we do this by publishing an attitude with zero timestamp
vehicle_attitude_s att{};
_att_pub.publish(att);
}
}
void EKF2::publish_yaw_estimator_status(const hrt_abstime &timestamp)
{
yaw_estimator_status_s yaw_est_test_data{};
static_assert(sizeof(yaw_estimator_status_s::yaw) / sizeof(float) == N_MODELS_EKFGSF,
"yaw_estimator_status_s::yaw wrong size");
if (_ekf.getDataEKFGSF(&yaw_est_test_data.yaw_composite, &yaw_est_test_data.yaw_variance,
&yaw_est_test_data.yaw[0],
&yaw_est_test_data.innov_vn[0], &yaw_est_test_data.innov_ve[0],
&yaw_est_test_data.weight[0])) {
yaw_est_test_data.timestamp = timestamp;
_yaw_est_pub.publish(yaw_est_test_data);
}
}
void EKF2::publish_wind_estimate(const hrt_abstime &timestamp)
{
if (_ekf.get_wind_status()) {
// Publish wind estimate only if ekf declares them valid
wind_estimate_s wind_estimate{};
const Vector2f wind_vel = _ekf.getWindVelocity();
const Vector2f wind_vel_var = _ekf.getWindVelocityVariance();
_ekf.getAirspeedInnov(wind_estimate.tas_innov);
_ekf.getAirspeedInnovVar(wind_estimate.tas_innov_var);
_ekf.getBetaInnov(wind_estimate.beta_innov);
_ekf.getBetaInnovVar(wind_estimate.beta_innov_var);
wind_estimate.timestamp = timestamp;
wind_estimate.windspeed_north = wind_vel(0);
wind_estimate.windspeed_east = wind_vel(1);
wind_estimate.variance_north = wind_vel_var(0);
wind_estimate.variance_east = wind_vel_var(1);
wind_estimate.tas_scale = 0.0f; //leave at 0 as scale is not estimated in ekf
_wind_pub.publish(wind_estimate);
}
}
bool EKF2::blend_gps_data()
{
// zero the blend weights
memset(&_blend_weights, 0, sizeof(_blend_weights));
/*
* If both receivers have the same update rate, use the oldest non-zero time.
* If two receivers with different update rates are used, use the slowest.
* If time difference is excessive, use newest to prevent a disconnected receiver
* from blocking updates.
*/
// Calculate the time step for each receiver with some filtering to reduce the effects of jitter
// Find the largest and smallest time step.
float dt_max = 0.0f;
float dt_min = 0.3f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
float raw_dt = 1e-6f * (float)(_gps_state[i].time_usec - _time_prev_us[i]);
if (raw_dt > 0.0f && raw_dt < 0.3f) {
_gps_dt[i] = 0.1f * raw_dt + 0.9f * _gps_dt[i];
}
if (_gps_dt[i] > dt_max) {
dt_max = _gps_dt[i];
_gps_slowest_index = i;
}
if (_gps_dt[i] < dt_min) {
dt_min = _gps_dt[i];
}
}
// Find the receiver that is last be updated
uint64_t max_us = 0; // newest non-zero system time of arrival of a GPS message
uint64_t min_us = -1; // oldest non-zero system time of arrival of a GPS message
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
// Find largest and smallest times
if (_gps_state[i].time_usec > max_us) {
max_us = _gps_state[i].time_usec;
_gps_newest_index = i;
}
if ((_gps_state[i].time_usec < min_us) && (_gps_state[i].time_usec > 0)) {
min_us = _gps_state[i].time_usec;
_gps_oldest_index = i;
}
}
if ((max_us - min_us) > 300000) {
// A receiver has timed out so fall out of blending
if (_gps_state[0].time_usec > _gps_state[1].time_usec) {
_gps_select_index = 0;
} else {
_gps_select_index = 1;
}
return false;
}
// One receiver has lost 3D fix, fall out of blending
if (_gps_state[0].fix_type > 2 && _gps_state[1].fix_type < 3) {
_gps_select_index = 0;
return false;
} else if (_gps_state[1].fix_type > 2 && _gps_state[0].fix_type < 3) {
_gps_select_index = 1;
return false;
}
/*
* If the largest dt is less than 20% greater than the smallest, then we have receivers
* running at the same rate then we wait until we have two messages with an arrival time
* difference that is less than 50% of the smallest time step and use the time stamp from
* the newest data.
* Else we have two receivers at different update rates and use the slowest receiver
* as the timing reference.
*/
if ((dt_max - dt_min) < 0.2f * dt_min) {
// both receivers assumed to be running at the same rate
if ((max_us - min_us) < (uint64_t)(5e5f * dt_min)) {
// data arrival within a short time window enables the two measurements to be blended
_gps_time_ref_index = _gps_newest_index;
_gps_new_output_data = true;
}
} else {
// both receivers running at different rates
_gps_time_ref_index = _gps_slowest_index;
if (_gps_state[_gps_time_ref_index].time_usec > _time_prev_us[_gps_time_ref_index]) {
// blend data at the rate of the slower receiver
_gps_new_output_data = true;
}
}
if (_gps_new_output_data) {
_gps_blended_state.time_usec = _gps_state[_gps_time_ref_index].time_usec;
// calculate the sum squared speed accuracy across all GPS sensors
float speed_accuracy_sum_sq = 0.0f;
if (_param_ekf2_gps_mask.get() & BLEND_MASK_USE_SPD_ACC) {
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].fix_type >= 3 && _gps_state[i].sacc > 0.0f) {
speed_accuracy_sum_sq += _gps_state[i].sacc * _gps_state[i].sacc;
} else {
// not all receivers support this metric so set it to zero and don't use it
speed_accuracy_sum_sq = 0.0f;
break;
}
}
}
// calculate the sum squared horizontal position accuracy across all GPS sensors
float horizontal_accuracy_sum_sq = 0.0f;
if (_param_ekf2_gps_mask.get() & BLEND_MASK_USE_HPOS_ACC) {
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].fix_type >= 2 && _gps_state[i].eph > 0.0f) {
horizontal_accuracy_sum_sq += _gps_state[i].eph * _gps_state[i].eph;
} else {
// not all receivers support this metric so set it to zero and don't use it
horizontal_accuracy_sum_sq = 0.0f;
break;
}
}
}
// calculate the sum squared vertical position accuracy across all GPS sensors
float vertical_accuracy_sum_sq = 0.0f;
if (_param_ekf2_gps_mask.get() & BLEND_MASK_USE_VPOS_ACC) {
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].fix_type >= 3 && _gps_state[i].epv > 0.0f) {
vertical_accuracy_sum_sq += _gps_state[i].epv * _gps_state[i].epv;
} else {
// not all receivers support this metric so set it to zero and don't use it
vertical_accuracy_sum_sq = 0.0f;
break;
}
}
}
// Check if we can do blending using reported accuracy
bool can_do_blending = (horizontal_accuracy_sum_sq > 0.0f || vertical_accuracy_sum_sq > 0.0f
|| speed_accuracy_sum_sq > 0.0f);
// if we can't do blending using reported accuracy, return false and hard switch logic will be used instead
if (!can_do_blending) {
return false;
}
float sum_of_all_weights = 0.0f;
// calculate a weighting using the reported speed accuracy
float spd_blend_weights[GPS_MAX_RECEIVERS] = {};
if (speed_accuracy_sum_sq > 0.0f && (_param_ekf2_gps_mask.get() & BLEND_MASK_USE_SPD_ACC)) {
// calculate the weights using the inverse of the variances
float sum_of_spd_weights = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].fix_type >= 3 && _gps_state[i].sacc >= 0.001f) {
spd_blend_weights[i] = 1.0f / (_gps_state[i].sacc * _gps_state[i].sacc);
sum_of_spd_weights += spd_blend_weights[i];
}
}
// normalise the weights
if (sum_of_spd_weights > 0.0f) {
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
spd_blend_weights[i] = spd_blend_weights[i] / sum_of_spd_weights;
}
sum_of_all_weights += 1.0f;
}
}
// calculate a weighting using the reported horizontal position
float hpos_blend_weights[GPS_MAX_RECEIVERS] = {};
if (horizontal_accuracy_sum_sq > 0.0f && (_param_ekf2_gps_mask.get() & BLEND_MASK_USE_HPOS_ACC)) {
// calculate the weights using the inverse of the variances
float sum_of_hpos_weights = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].fix_type >= 2 && _gps_state[i].eph >= 0.001f) {
hpos_blend_weights[i] = horizontal_accuracy_sum_sq / (_gps_state[i].eph * _gps_state[i].eph);
sum_of_hpos_weights += hpos_blend_weights[i];
}
}
// normalise the weights
if (sum_of_hpos_weights > 0.0f) {
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
hpos_blend_weights[i] = hpos_blend_weights[i] / sum_of_hpos_weights;
}
sum_of_all_weights += 1.0f;
}
}
// calculate a weighting using the reported vertical position accuracy
float vpos_blend_weights[GPS_MAX_RECEIVERS] = {};
if (vertical_accuracy_sum_sq > 0.0f && (_param_ekf2_gps_mask.get() & BLEND_MASK_USE_VPOS_ACC)) {
// calculate the weights using the inverse of the variances
float sum_of_vpos_weights = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].fix_type >= 3 && _gps_state[i].epv >= 0.001f) {
vpos_blend_weights[i] = vertical_accuracy_sum_sq / (_gps_state[i].epv * _gps_state[i].epv);
sum_of_vpos_weights += vpos_blend_weights[i];
}
}
// normalise the weights
if (sum_of_vpos_weights > 0.0f) {
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
vpos_blend_weights[i] = vpos_blend_weights[i] / sum_of_vpos_weights;
}
sum_of_all_weights += 1.0f;
};
}
// calculate an overall weight
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
_blend_weights[i] = (hpos_blend_weights[i] + vpos_blend_weights[i] + spd_blend_weights[i]) / sum_of_all_weights;
}
// With updated weights we can calculate a blended GPS solution and
// offsets for each physical receiver
update_gps_blend_states();
update_gps_offsets();
_gps_select_index = 2;
}
return true;
}
/*
* Update the internal state estimate for a blended GPS solution that is a weighted average of the phsyical receiver solutions
* with weights are calculated in calc_gps_blend_weights(). This internal state cannot be used directly by estimators
* because if physical receivers have significant position differences, variation in receiver estimated accuracy will
* cause undesirable variation in the position solution.
*/
void EKF2::update_gps_blend_states()
{
// initialise the blended states so we can accumulate the results using the weightings for each GPS receiver.
_gps_blended_state.time_usec = 0;
_gps_blended_state.lat = 0;
_gps_blended_state.lon = 0;
_gps_blended_state.alt = 0;
_gps_blended_state.fix_type = 0;
_gps_blended_state.eph = FLT_MAX;
_gps_blended_state.epv = FLT_MAX;
_gps_blended_state.sacc = FLT_MAX;
_gps_blended_state.vel_m_s = 0.0f;
_gps_blended_state.vel_ned.setZero();
_gps_blended_state.vel_ned_valid = true;
_gps_blended_state.nsats = 0;
_gps_blended_state.pdop = FLT_MAX;
_blended_antenna_offset.zero();
// combine the the GPS states into a blended solution using the weights calculated in calc_blend_weights()
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
// blend the timing data
_gps_blended_state.time_usec += (uint64_t)((double)_gps_state[i].time_usec * (double)_blend_weights[i]);
// use the highest status
if (_gps_state[i].fix_type > _gps_blended_state.fix_type) {
_gps_blended_state.fix_type = _gps_state[i].fix_type;
}
// calculate a blended average speed and velocity vector
_gps_blended_state.vel_m_s += _gps_state[i].vel_m_s * _blend_weights[i];
_gps_blended_state.vel_ned += _gps_state[i].vel_ned * _blend_weights[i];
// Assume blended error magnitude, DOP and sat count is equal to the best value from contributing receivers
// If any receiver contributing has an invalid velocity, then report blended velocity as invalid
if (_blend_weights[i] > 0.0f) {
if (_gps_state[i].eph > 0.0f
&& _gps_state[i].eph < _gps_blended_state.eph) {
_gps_blended_state.eph = _gps_state[i].eph;
}
if (_gps_state[i].epv > 0.0f
&& _gps_state[i].epv < _gps_blended_state.epv) {
_gps_blended_state.epv = _gps_state[i].epv;
}
if (_gps_state[i].sacc > 0.0f
&& _gps_state[i].sacc < _gps_blended_state.sacc) {
_gps_blended_state.sacc = _gps_state[i].sacc;
}
if (_gps_state[i].pdop > 0
&& _gps_state[i].pdop < _gps_blended_state.pdop) {
_gps_blended_state.pdop = _gps_state[i].pdop;
}
if (_gps_state[i].nsats > 0
&& _gps_state[i].nsats > _gps_blended_state.nsats) {
_gps_blended_state.nsats = _gps_state[i].nsats;
}
if (!_gps_state[i].vel_ned_valid) {
_gps_blended_state.vel_ned_valid = false;
}
}
// TODO read parameters for individual GPS antenna positions and blend
// Vector3f temp_antenna_offset = _antenna_offset[i];
// temp_antenna_offset *= _blend_weights[i];
// _blended_antenna_offset += temp_antenna_offset;
}
/*
* Calculate the instantaneous weighted average location using available GPS instances and store in _gps_state.
* This is statistically the most likely location, but may not be stable enough for direct use by the EKF.
*/
// Use the GPS with the highest weighting as the reference position
float best_weight = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_blend_weights[i] > best_weight) {
best_weight = _blend_weights[i];
_gps_best_index = i;
_gps_blended_state.lat = _gps_state[i].lat;
_gps_blended_state.lon = _gps_state[i].lon;
_gps_blended_state.alt = _gps_state[i].alt;
}
}
// Convert each GPS position to a local NEU offset relative to the reference position
Vector2f blended_NE_offset_m;
blended_NE_offset_m.zero();
float blended_alt_offset_mm = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if ((_blend_weights[i] > 0.0f) && (i != _gps_best_index)) {
// calculate the horizontal offset
Vector2f horiz_offset{};
get_vector_to_next_waypoint((_gps_blended_state.lat / 1.0e7),
(_gps_blended_state.lon / 1.0e7), (_gps_state[i].lat / 1.0e7), (_gps_state[i].lon / 1.0e7),
&horiz_offset(0), &horiz_offset(1));
// sum weighted offsets
blended_NE_offset_m += horiz_offset * _blend_weights[i];
// calculate vertical offset
float vert_offset = (float)(_gps_state[i].alt - _gps_blended_state.alt);
// sum weighted offsets
blended_alt_offset_mm += vert_offset * _blend_weights[i];
}
}
// Add the sum of weighted offsets to the reference position to obtain the blended position
double lat_deg_now = (double)_gps_blended_state.lat * 1.0e-7;
double lon_deg_now = (double)_gps_blended_state.lon * 1.0e-7;
double lat_deg_res, lon_deg_res;
add_vector_to_global_position(lat_deg_now, lon_deg_now, blended_NE_offset_m(0), blended_NE_offset_m(1), &lat_deg_res,
&lon_deg_res);
_gps_blended_state.lat = (int32_t)(1.0E7 * lat_deg_res);
_gps_blended_state.lon = (int32_t)(1.0E7 * lon_deg_res);
_gps_blended_state.alt += (int32_t)blended_alt_offset_mm;
// Take GPS heading from the highest weighted receiver that is publishing a valid .heading value
uint8_t gps_best_yaw_index = 0;
best_weight = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (PX4_ISFINITE(_gps_state[i].yaw) && (_blend_weights[i] > best_weight)) {
best_weight = _blend_weights[i];
gps_best_yaw_index = i;
}
}
_gps_blended_state.yaw = _gps_state[gps_best_yaw_index].yaw;
_gps_blended_state.yaw_offset = _gps_state[gps_best_yaw_index].yaw_offset;
}
/*
* The location in _gps_blended_state will move around as the relative accuracy changes.
* To mitigate this effect a low-pass filtered offset from each GPS location to the blended location is
* calculated.
*/
void EKF2::update_gps_offsets()
{
// Calculate filter coefficients to be applied to the offsets for each GPS position and height offset
// A weighting of 1 will make the offset adjust the slowest, a weighting of 0 will make it adjust with zero filtering
float alpha[GPS_MAX_RECEIVERS] = {};
float omega_lpf = 1.0f / fmaxf(_param_ekf2_gps_tau.get(), 1.0f);
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_gps_state[i].time_usec - _time_prev_us[i] > 0) {
// calculate the filter coefficient that achieves the time constant specified by the user adjustable parameter
alpha[i] = constrain(omega_lpf * 1e-6f * (float)(_gps_state[i].time_usec - _time_prev_us[i]),
0.0f, 1.0f);
_time_prev_us[i] = _gps_state[i].time_usec;
}
}
// Calculate a filtered position delta for each GPS relative to the blended solution state
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
Vector2f offset;
get_vector_to_next_waypoint((_gps_state[i].lat / 1.0e7), (_gps_state[i].lon / 1.0e7),
(_gps_blended_state.lat / 1.0e7), (_gps_blended_state.lon / 1.0e7), &offset(0), &offset(1));
_NE_pos_offset_m[i] = offset * alpha[i] + _NE_pos_offset_m[i] * (1.0f - alpha[i]);
_hgt_offset_mm[i] = (float)(_gps_blended_state.alt - _gps_state[i].alt) * alpha[i] +
_hgt_offset_mm[i] * (1.0f - alpha[i]);
}
// calculate offset limits from the largest difference between receivers
Vector2f max_ne_offset{};
float max_alt_offset = 0;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
for (uint8_t j = i; j < GPS_MAX_RECEIVERS; j++) {
if (i != j) {
Vector2f offset;
get_vector_to_next_waypoint((_gps_state[i].lat / 1.0e7), (_gps_state[i].lon / 1.0e7),
(_gps_state[j].lat / 1.0e7), (_gps_state[j].lon / 1.0e7), &offset(0), &offset(1));
max_ne_offset(0) = fmaxf(max_ne_offset(0), fabsf(offset(0)));
max_ne_offset(1) = fmaxf(max_ne_offset(1), fabsf(offset(1)));
max_alt_offset = fmaxf(max_alt_offset, fabsf((float)(_gps_state[i].alt - _gps_state[j].alt)));
}
}
}
// apply offset limits
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
_NE_pos_offset_m[i](0) = constrain(_NE_pos_offset_m[i](0), -max_ne_offset(0), max_ne_offset(0));
_NE_pos_offset_m[i](1) = constrain(_NE_pos_offset_m[i](1), -max_ne_offset(1), max_ne_offset(1));
_hgt_offset_mm[i] = constrain(_hgt_offset_mm[i], -max_alt_offset, max_alt_offset);
}
}
/*
* Apply the steady state physical receiver offsets calculated by update_gps_offsets().
*/
void EKF2::apply_gps_offsets()
{
// calculate offset corrected output for each physical GPS.
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
// Add the sum of weighted offsets to the reference position to obtain the blended position
double lat_deg_now = (double)_gps_state[i].lat * 1.0e-7;
double lon_deg_now = (double)_gps_state[i].lon * 1.0e-7;
double lat_deg_res, lon_deg_res;
add_vector_to_global_position(lat_deg_now, lon_deg_now, _NE_pos_offset_m[i](0), _NE_pos_offset_m[i](1), &lat_deg_res,
&lon_deg_res);
_gps_output[i].lat = (int32_t)(1.0E7 * lat_deg_res);
_gps_output[i].lon = (int32_t)(1.0E7 * lon_deg_res);
_gps_output[i].alt = _gps_state[i].alt + (int32_t)_hgt_offset_mm[i];
// other receiver data is used uncorrected
_gps_output[i].time_usec = _gps_state[i].time_usec;
_gps_output[i].fix_type = _gps_state[i].fix_type;
_gps_output[i].vel_m_s = _gps_state[i].vel_m_s;
_gps_output[i].vel_ned = _gps_state[i].vel_ned;
_gps_output[i].eph = _gps_state[i].eph;
_gps_output[i].epv = _gps_state[i].epv;
_gps_output[i].sacc = _gps_state[i].sacc;
_gps_output[i].pdop = _gps_state[i].pdop;
_gps_output[i].nsats = _gps_state[i].nsats;
_gps_output[i].vel_ned_valid = _gps_state[i].vel_ned_valid;
_gps_output[i].yaw = _gps_state[i].yaw;
_gps_output[i].yaw_offset = _gps_state[i].yaw_offset;
}
}
/*
Calculate GPS output that is a blend of the offset corrected physical receiver data
*/
void EKF2::calc_gps_blend_output()
{
// Convert each GPS position to a local NEU offset relative to the reference position
// which is defined as the positon of the blended solution calculated from non offset corrected data
Vector2f blended_NE_offset_m;
blended_NE_offset_m.zero();
float blended_alt_offset_mm = 0.0f;
for (uint8_t i = 0; i < GPS_MAX_RECEIVERS; i++) {
if (_blend_weights[i] > 0.0f) {
// calculate the horizontal offset
Vector2f horiz_offset{};
get_vector_to_next_waypoint((_gps_blended_state.lat / 1.0e7),
(_gps_blended_state.lon / 1.0e7),
(_gps_output[i].lat / 1.0e7),
(_gps_output[i].lon / 1.0e7),
&horiz_offset(0),
&horiz_offset(1));
// sum weighted offsets
blended_NE_offset_m += horiz_offset * _blend_weights[i];
// calculate vertical offset
float vert_offset = (float)(_gps_output[i].alt - _gps_blended_state.alt);
// sum weighted offsets
blended_alt_offset_mm += vert_offset * _blend_weights[i];
}
}
// Add the sum of weighted offsets to the reference position to obtain the blended position
double lat_deg_now = (double)_gps_blended_state.lat * 1.0e-7;
double lon_deg_now = (double)_gps_blended_state.lon * 1.0e-7;
double lat_deg_res, lon_deg_res;
add_vector_to_global_position(lat_deg_now, lon_deg_now, blended_NE_offset_m(0), blended_NE_offset_m(1), &lat_deg_res,
&lon_deg_res);
_gps_output[GPS_BLENDED_INSTANCE].lat = (int32_t)(1.0E7 * lat_deg_res);
_gps_output[GPS_BLENDED_INSTANCE].lon = (int32_t)(1.0E7 * lon_deg_res);
_gps_output[GPS_BLENDED_INSTANCE].alt = _gps_blended_state.alt + (int32_t)blended_alt_offset_mm;
// Copy remaining data from internal states to output
_gps_output[GPS_BLENDED_INSTANCE].time_usec = _gps_blended_state.time_usec;
_gps_output[GPS_BLENDED_INSTANCE].fix_type = _gps_blended_state.fix_type;
_gps_output[GPS_BLENDED_INSTANCE].vel_m_s = _gps_blended_state.vel_m_s;
_gps_output[GPS_BLENDED_INSTANCE].vel_ned = _gps_blended_state.vel_ned;
_gps_output[GPS_BLENDED_INSTANCE].eph = _gps_blended_state.eph;
_gps_output[GPS_BLENDED_INSTANCE].epv = _gps_blended_state.epv;
_gps_output[GPS_BLENDED_INSTANCE].sacc = _gps_blended_state.sacc;
_gps_output[GPS_BLENDED_INSTANCE].pdop = _gps_blended_state.pdop;
_gps_output[GPS_BLENDED_INSTANCE].nsats = _gps_blended_state.nsats;
_gps_output[GPS_BLENDED_INSTANCE].vel_ned_valid = _gps_blended_state.vel_ned_valid;
_gps_output[GPS_BLENDED_INSTANCE].yaw = _gps_blended_state.yaw;
_gps_output[GPS_BLENDED_INSTANCE].yaw_offset = _gps_blended_state.yaw_offset;
}
float EKF2::filter_altitude_ellipsoid(float amsl_hgt)
{
float height_diff = static_cast<float>(_gps_alttitude_ellipsoid[0]) * 1e-3f - amsl_hgt;
if (_gps_alttitude_ellipsoid_previous_timestamp[0] == 0) {
_wgs84_hgt_offset = height_diff;
_gps_alttitude_ellipsoid_previous_timestamp[0] = _gps_state[0].time_usec;
} else if (_gps_state[0].time_usec != _gps_alttitude_ellipsoid_previous_timestamp[0]) {
// apply a 10 second first order low pass filter to baro offset
float dt = 1e-6f * static_cast<float>(_gps_state[0].time_usec - _gps_alttitude_ellipsoid_previous_timestamp[0]);
_gps_alttitude_ellipsoid_previous_timestamp[0] = _gps_state[0].time_usec;
float offset_rate_correction = 0.1f * (height_diff - _wgs84_hgt_offset);
_wgs84_hgt_offset += dt * math::constrain(offset_rate_correction, -0.1f, 0.1f);
}
return amsl_hgt + _wgs84_hgt_offset;
}
int EKF2::custom_command(int argc, char *argv[])
{
return print_usage("unknown command");
}
int EKF2::task_spawn(int argc, char *argv[])
{
bool replay_mode = false;
if (argc > 1 && !strcmp(argv[1], "-r")) {
PX4_INFO("replay mode enabled");
replay_mode = true;
}
EKF2 *instance = new EKF2(replay_mode);
if (instance) {
_object.store(instance);
_task_id = task_id_is_work_queue;
if (instance->init()) {
return PX4_OK;
}
} else {
PX4_ERR("alloc failed");
}
delete instance;
_object.store(nullptr);
_task_id = -1;
return PX4_ERROR;
}
int EKF2::print_usage(const char *reason)
{
if (reason) {
PX4_WARN("%s\n", reason);
}
PRINT_MODULE_DESCRIPTION(
R"DESCR_STR(
### Description
Attitude and position estimator using an Extended Kalman Filter. It is used for Multirotors and Fixed-Wing.
The documentation can be found on the [ECL/EKF Overview & Tuning](https://docs.px4.io/master/en/advanced_config/tuning_the_ecl_ekf.html) page.
ekf2 can be started in replay mode (`-r`): in this mode it does not access the system time, but only uses the
timestamps from the sensor topics.
)DESCR_STR");
PRINT_MODULE_USAGE_NAME("ekf2", "estimator");
PRINT_MODULE_USAGE_COMMAND("start");
PRINT_MODULE_USAGE_PARAM_FLAG('r', "Enable replay mode", true);
PRINT_MODULE_USAGE_DEFAULT_COMMANDS();
return 0;
}
extern "C" __EXPORT int ekf2_main(int argc, char *argv[])
{
return EKF2::main(argc, argv);
}
Reference in New Issue View Git Blame Copy Permalink