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Range check cleanup (#782)

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* EKF: centralize range finder tilt check

* Ekf-control: do not double check for terrain estimate validity

isRangeAidSuitable can only return true if the terrain estimate is valid
so there is no need for an additional check

* range_finder_checks: restructure the checks to avoid early returns

There is now only one clear path that can lead to the validity being
true.
Furthermore, if the _rng_hgt_valid is true, we can trust it and we don't
need for additional checks such as tilt.

The case where we need to provide fake measurements because the drone is
on the ground and the range finder data is bad is already handled
in "controlHeightFusion" so there is no need to hack the range finder
checks with that.

* Add Sensor and SensorRangeFinder classes

The purpose is to encapsulate the checks for each sensor in a dedicated
class with the same interface

* SensorRangeFinder: encapsulate in estimator::sensor namespace

* EKF: rename _sensor_rng to _range_sensor

* Range checks: include limits in valid range

* RangeChecks: update comment in the continuity checks

* RangeChecks: move more low-level checks in functions

Also move setTilt out of the terrain estimator, this is anyway protected internally
to not compute cos/sin if the parameter did not change.

* Sensor: remove unused virtual functions

Those are not required yet but can still be added later

* SensorRangeFinder: re-organise member variables

Also rename getRangeToEarth to getCosTilt

* SensorRangeFinder: split setSensorTilt and setCosMaxTilt functions

* SensorRangeFinder: Add a few unit tests

- good data
- tilt exceeded
- max range exceeded

* SensorRangeFinder: set hysteresis in us instead of ms

* SensorRangeFinder: Add more tests

* SensorRangeFinder: update continuity, hysteresis and stuck tests

* SensorRangeFinder: rename variables

* SensorRangeFinder: get rid of "delayed" specification

From the SensorRangeFinder class point of view, it's not relevant to
know if the data is delayed or not

* SensorRangeFinder: move time_last_valid out of stuck check

* SensorRangeFinder: rename file names to sensor_range_finder

* SensorRangeFinder: address Kamil's comments

* SensorRangeFinder: Add more tilt tests

* SensorRangeFinder: store current tilt offset

This is to avoid recomputing cos/sin functions at each loop
This commit is contained in:
Mathieu Bresciani
2020-04-03 08:28:07 +02:00
committed by GitHub
parent 544385fd8c
commit 0623b2b02d
15 changed files with 747 additions and 263 deletions
Download Patch File Download Diff File Expand all files Collapse all files
EKF/terrain_estimator.cpp
+38 -49
View File
@@ -57,11 +57,11 @@ bool Ekf::initHagl()
initialized = true;
} else if ((_params.terrain_fusion_mode & TerrainFusionMask::TerrainFuseRangeFinder)
&& _rng_hgt_valid
&& isRecent(latest_measurement.time_us, (uint64_t)2e5)
&& _R_rng_to_earth_2_2 > _params.range_cos_max_tilt) {
&& _range_sensor.isHealthy()
&& isRecent(latest_measurement.time_us, (uint64_t)2e5)) {
// if we have a fresh measurement, use it to initialise the terrain estimator
_terrain_vpos = _state.pos(2) + latest_measurement.rng * _R_rng_to_earth_2_2;
// TODO: the latest measurment did not go through the checks and could be invalid!
_terrain_vpos = _state.pos(2) + latest_measurement.rng * _range_sensor.getCosTilt();
// initialise state variance to variance of measurement
_terrain_var = sq(_params.range_noise);
// success
@@ -113,13 +113,8 @@ void Ekf::runTerrainEstimator()
_terrain_var = math::constrain(_terrain_var, 0.0f, 1e4f);
// Fuse range finder data if available
if (_range_data_ready && _rng_hgt_valid) {
if (_range_sensor.isDataHealthy()) {
fuseHagl();
// update range sensor angle parameters in case they have changed
// we do this here to avoid doing those calculations at a high rate
_sin_tilt_rng = sinf(_params.rng_sens_pitch);
_cos_tilt_rng = cosf(_params.rng_sens_pitch);
}
if (_flow_for_terrain_data_ready) {
@@ -138,53 +133,47 @@ void Ekf::runTerrainEstimator()
void Ekf::fuseHagl()
{
// If the vehicle is excessively tilted, do not try to fuse range finder observations
if (_R_rng_to_earth_2_2 > _params.range_cos_max_tilt) {
// get a height above ground measurement from the range finder assuming a flat earth
const float meas_hagl = _range_sample_delayed.rng * _R_rng_to_earth_2_2;
// get a height above ground measurement from the range finder assuming a flat earth
const float meas_hagl = _range_sensor.getRange() * _range_sensor.getCosTilt();
// predict the hagl from the vehicle position and terrain height
const float pred_hagl = _terrain_vpos - _state.pos(2);
// predict the hagl from the vehicle position and terrain height
const float pred_hagl = _terrain_vpos - _state.pos(2);
// calculate the innovation
_hagl_innov = pred_hagl - meas_hagl;
// calculate the innovation
_hagl_innov = pred_hagl - meas_hagl;
// calculate the observation variance adding the variance of the vehicles own height uncertainty
const float obs_variance = fmaxf(P(9,9) * _params.vehicle_variance_scaler, 0.0f)
+ sq(_params.range_noise)
+ sq(_params.range_noise_scaler * _range_sample_delayed.rng);
// calculate the observation variance adding the variance of the vehicles own height uncertainty
const float obs_variance = fmaxf(P(9,9) * _params.vehicle_variance_scaler, 0.0f)
+ sq(_params.range_noise)
+ sq(_params.range_noise_scaler * _range_sensor.getRange());
// calculate the innovation variance - limiting it to prevent a badly conditioned fusion
_hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance);
// calculate the innovation variance - limiting it to prevent a badly conditioned fusion
_hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance);
// perform an innovation consistency check and only fuse data if it passes
const float gate_size = fmaxf(_params.range_innov_gate, 1.0f);
_hagl_test_ratio = sq(_hagl_innov) / (sq(gate_size) * _hagl_innov_var);
// perform an innovation consistency check and only fuse data if it passes
const float gate_size = fmaxf(_params.range_innov_gate, 1.0f);
_hagl_test_ratio = sq(_hagl_innov) / (sq(gate_size) * _hagl_innov_var);
if (_hagl_test_ratio <= 1.0f) {
// calculate the Kalman gain
float gain = _terrain_var / _hagl_innov_var;
// correct the state
_terrain_vpos -= gain * _hagl_innov;
// correct the variance
_terrain_var = fmaxf(_terrain_var * (1.0f - gain), 0.0f);
// record last successful fusion event
_time_last_hagl_fuse = _time_last_imu;
_innov_check_fail_status.flags.reject_hagl = false;
} else {
// If we have been rejecting range data for too long, reset to measurement
if (isTimedOut(_time_last_hagl_fuse, (uint64_t)10E6)) {
_terrain_vpos = _state.pos(2) + meas_hagl;
_terrain_var = obs_variance;
} else {
_innov_check_fail_status.flags.reject_hagl = true;
}
}
if (_hagl_test_ratio <= 1.0f) {
// calculate the Kalman gain
float gain = _terrain_var / _hagl_innov_var;
// correct the state
_terrain_vpos -= gain * _hagl_innov;
// correct the variance
_terrain_var = fmaxf(_terrain_var * (1.0f - gain), 0.0f);
// record last successful fusion event
_time_last_hagl_fuse = _time_last_imu;
_innov_check_fail_status.flags.reject_hagl = false;
} else {
_innov_check_fail_status.flags.reject_hagl = true;
// If we have been rejecting range data for too long, reset to measurement
if (isTimedOut(_time_last_hagl_fuse, (uint64_t)10E6)) {
_terrain_vpos = _state.pos(2) + meas_hagl;
_terrain_var = obs_variance;
} else {
_innov_check_fail_status.flags.reject_hagl = true;
}
}
}