2021-1-capstone-design1 / JJS_Project2

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f8ff5ebd 1 parent 1fd483af

feat : caculate rightinclination and compare current inclination

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Showing 2 changed files with 20 additions and 435 deletions
......@@ -30,7 +30,6 @@
* POSSIBILITY OF SUCH DAMAGE.
*
****************************************************************************/
#include "EKF2.hpp"
#include <iostream>
#include <px4_platform_common/defines.h>
......@@ -165,7 +164,6 @@ EKF2::EKF2(bool multi_mode, const px4::wq_config_t &config, bool replay_mode):
_param_ekf2_gsf_tas_default(_params->EKFGSF_tas_default)
{
}
EKF2::~EKF2()
{
perf_free(_ecl_ekf_update_perf);
......@@ -173,7 +171,6 @@ EKF2::~EKF2()
perf_free(_imu_missed_perf);
perf_free(_mag_missed_perf);
}
bool EKF2::multi_init(int imu, int mag)
{
// advertise immediately to ensure consistent uORB instance numbering
......@@ -182,7 +179,6 @@ bool EKF2::multi_init(int imu, int mag)
_global_position_pub.advertise();
_odometry_pub.advertise();
_wind_pub.advertise();
_ekf2_timestamps_pub.advertise();
_ekf_gps_drift_pub.advertise();
_estimator_innovation_test_ratios_pub.advertise();
......@@ -195,28 +191,20 @@ bool EKF2::multi_init(int imu, int mag)
_estimator_status_flags_pub.advertise();
_estimator_visual_odometry_aligned_pub.advertised();
_yaw_est_pub.advertise();
bool changed_instance = _vehicle_imu_sub.ChangeInstance(imu) && _magnetometer_sub.ChangeInstance(mag);
const int status_instance = _estimator_states_pub.get_instance();
if ((status_instance >= 0) && changed_instance
&& (_attitude_pub.get_instance() == status_instance)
&& (_local_position_pub.get_instance() == status_instance)
&& (_global_position_pub.get_instance() == status_instance)) {
_instance = status_instance;
ScheduleNow();
return true;
}
PX4_ERR("publication instance problem: %d att: %d lpos: %d gpos: %d", status_instance,
_attitude_pub.get_instance(), _local_position_pub.get_instance(), _global_position_pub.get_instance());
return false;
}
int EKF2::print_status()
{
PX4_INFO_RAW("ekf2:%d attitude: %d, local position: %d, global position: %d\n", _instance, _ekf.attitude_valid(),
......@@ -224,71 +212,54 @@ int EKF2::print_status()
perf_print_counter(_ecl_ekf_update_perf);
perf_print_counter(_ecl_ekf_update_full_perf);
perf_print_counter(_imu_missed_perf);
if (_device_id_mag != 0) {
perf_print_counter(_mag_missed_perf);
}
return 0;
}
void EKF2::Run()
{
if (should_exit()) {
_sensor_combined_sub.unregisterCallback();
_vehicle_imu_sub.unregisterCallback();
return;
}
// check for parameter updates
if (_parameter_update_sub.updated() || !_callback_registered) {
// clear update
parameter_update_s pupdate;
_parameter_update_sub.copy(&pupdate);
// update parameters from storage
updateParams();
_ekf.set_min_required_gps_health_time(_param_ekf2_req_gps_h.get() * 1_s);
// The airspeed scale factor correcton is only available via parameter as used by the airspeed module
param_t param_aspd_scale = param_find("ASPD_SCALE");
if (param_aspd_scale != PARAM_INVALID) {
param_get(param_aspd_scale, &_airspeed_scale_factor);
}
}
if (!_callback_registered) {
if (_multi_mode) {
_callback_registered = _vehicle_imu_sub.registerCallback();
} else {
_callback_registered = _sensor_combined_sub.registerCallback();
}
if (!_callback_registered) {
PX4_WARN("%d - failed to register callback, retrying", _instance);
ScheduleDelayed(1_s);
return;
}
}
if (_vehicle_command_sub.updated()) {
vehicle_command_s vehicle_command;
if (_vehicle_command_sub.update(&vehicle_command)) {
if (vehicle_command.command == vehicle_command_s::VEHICLE_CMD_SET_GPS_GLOBAL_ORIGIN) {
if (!_ekf.control_status_flags().in_air) {
uint64_t origin_time {};
double latitude = vehicle_command.param5;
double longitude = vehicle_command.param6;
float altitude = vehicle_command.param7;
_ekf.setEkfGlobalOrigin(latitude, longitude, altitude);
// Validate the ekf origin status.
_ekf.getEkfGlobalOrigin(origin_time, latitude, longitude, altitude);
PX4_INFO("New NED origin (LLA): %3.10f, %3.10f, %4.3f\n", latitude, longitude, static_cast<double>(altitude));
......@@ -296,81 +267,63 @@ void EKF2::Run()
}
}
}
bool imu_updated = false;
imuSample imu_sample_new {};
hrt_abstime imu_dt = 0; // for tracking time slip later
if (_multi_mode) {
const unsigned last_generation = _vehicle_imu_sub.get_last_generation();
vehicle_imu_s imu;
imu_updated = _vehicle_imu_sub.update(&imu);
if (imu_updated && (_vehicle_imu_sub.get_last_generation() != last_generation + 1)) {
perf_count(_imu_missed_perf);
PX4_DEBUG("%d - vehicle_imu lost, generation %d -> %d", _instance, last_generation,
_vehicle_imu_sub.get_last_generation());
}
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;
if ((_device_id_accel == 0) || (_device_id_gyro == 0)) {
_device_id_accel = imu.accel_device_id;
_device_id_gyro = imu.gyro_device_id;
_imu_calibration_count = imu.calibration_count;
} else if ((imu.calibration_count > _imu_calibration_count)
|| (imu.accel_device_id != _device_id_accel)
|| (imu.gyro_device_id != _device_id_gyro)) {
PX4_INFO("%d - resetting IMU bias", _instance);
_device_id_accel = imu.accel_device_id;
_device_id_gyro = imu.gyro_device_id;
_ekf.resetImuBias();
_imu_calibration_count = imu.calibration_count;
}
} else {
sensor_combined_s sensor_combined;
imu_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 (_sensor_selection_sub.updated() || (_device_id_accel == 0 || _device_id_gyro == 0)) {
sensor_selection_s sensor_selection;
if (_sensor_selection_sub.copy(&sensor_selection)) {
if (_device_id_accel != sensor_selection.accel_device_id) {
_ekf.resetAccelBias();
_device_id_accel = sensor_selection.accel_device_id;
}
if (_device_id_gyro != sensor_selection.gyro_device_id) {
_ekf.resetGyroBias();
_device_id_gyro = sensor_selection.gyro_device_id;
......@@ -378,72 +331,54 @@ void EKF2::Run()
}
}
}
if (imu_updated) {
const hrt_abstime now = imu_sample_new.time_us;
// push imu data into estimator
_ekf.setIMUData(imu_sample_new);
PublishAttitude(now); // publish attitude immediately (uses quaternion from output predictor)
// integrate time to monitor time slippage
if (_start_time_us > 0) {
_integrated_time_us += imu_dt;
_last_time_slip_us = (imu_sample_new.time_us - _start_time_us) - _integrated_time_us;
} else {
_start_time_us = imu_sample_new.time_us;
_last_time_slip_us = 0;
}
// update all other topics if they have new data
if (_status_sub.updated()) {
vehicle_status_s vehicle_status;
if (_status_sub.copy(&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);
_preflt_checker.setVehicleCanObserveHeadingInFlight(vehicle_status.vehicle_type !=
vehicle_status_s::VEHICLE_TYPE_ROTARY_WING);
_armed = (vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_ARMED);
// update standby (arming state) flag
const bool standby = (vehicle_status.arming_state == vehicle_status_s::ARMING_STATE_STANDBY);
if (_standby != standby) {
_standby = standby;
// reset preflight checks if transitioning in or out of standby arming state
_preflt_checker.reset();
}
}
}
if (_vehicle_land_detected_sub.updated()) {
vehicle_land_detected_s vehicle_land_detected;
if (_vehicle_land_detected_sub.copy(&vehicle_land_detected)) {
_ekf.set_in_air_status(!vehicle_land_detected.landed);
if (_armed && (_param_ekf2_gnd_eff_dz.get() > 0.f)) {
if (!_had_valid_terrain) {
// update ground effect flag based on land detector state if we've never had valid terrain data
_ekf.set_gnd_effect_flag(vehicle_land_detected.in_ground_effect);
}
} else {
_ekf.set_gnd_effect_flag(false);
}
}
}
// ekf2_timestamps (using 0.1 ms relative timestamps)
ekf2_timestamps_s ekf2_timestamps {
.timestamp = now,
......@@ -454,33 +389,25 @@ void EKF2::Run()
.vehicle_magnetometer_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID,
.visual_odometry_timestamp_rel = ekf2_timestamps_s::RELATIVE_TIMESTAMP_INVALID,
};
UpdateAirspeedSample(ekf2_timestamps);
UpdateAuxVelSample(ekf2_timestamps);
UpdateBaroSample(ekf2_timestamps);
UpdateGpsSample(ekf2_timestamps);
UpdateMagSample(ekf2_timestamps);
UpdateRangeSample(ekf2_timestamps);
vehicle_odometry_s ev_odom;
const bool new_ev_odom = UpdateExtVisionSample(ekf2_timestamps, ev_odom);
optical_flow_s optical_flow;
const bool new_optical_flow = UpdateFlowSample(ekf2_timestamps, optical_flow);
// run the EKF update and output
const hrt_abstime ekf_update_start = hrt_absolute_time();
if (_ekf.update()) {
perf_set_elapsed(_ecl_ekf_update_full_perf, hrt_elapsed_time(&ekf_update_start));
PublishLocalPosition(now);
PublishOdometry(now, imu_sample_new);
PublishGlobalPosition(now);
PublishSensorBias(now);
PublishWindEstimate(now);
// publish status/logging messages
PublishEkfDriftMetrics(now);
PublishEventFlags(now);
......@@ -491,28 +418,22 @@ void EKF2::Run()
PublishInnovationTestRatios(now);
PublishInnovationVariances(now);
PublishYawEstimatorStatus(now);
UpdateMagCalibration(now);
} else {
// ekf no update
perf_set_elapsed(_ecl_ekf_update_perf, hrt_elapsed_time(&ekf_update_start));
}
// publish external visual odometry after fixed frame alignment if new odometry is received
if (new_ev_odom) {
PublishOdometryAligned(now, ev_odom);
}
if (new_optical_flow) {
PublishOpticalFlowVel(now, optical_flow);
}
// publish ekf2_timestamps
_ekf2_timestamps_pub.publish(ekf2_timestamps);
}
}
void EKF2::PublishAttitude(const hrt_abstime &timestamp)
{
if (_ekf.attitude_valid()) {
......@@ -521,11 +442,9 @@ void EKF2::PublishAttitude(const hrt_abstime &timestamp)
att.timestamp_sample = 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.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_attitude_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
......@@ -533,13 +452,11 @@ void EKF2::PublishAttitude(const hrt_abstime &timestamp)
_attitude_pub.publish(att);
}
}
void EKF2::PublishEkfDriftMetrics(const hrt_abstime &timestamp)
{
// 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.hpos_drift_rate = gps_drift[0];
......@@ -547,35 +464,28 @@ void EKF2::PublishEkfDriftMetrics(const hrt_abstime &timestamp)
drift_data.hspd = gps_drift[2];
drift_data.blocked = blocked;
drift_data.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_ekf_gps_drift_pub.publish(drift_data);
}
}
void EKF2::PublishEventFlags(const hrt_abstime &timestamp)
{
// information events
uint32_t information_events = _ekf.information_event_status().value;
bool information_event_updated = false;
if (information_events != 0) {
information_event_updated = true;
_filter_information_event_changes++;
}
// warning events
uint32_t warning_events = _ekf.warning_event_status().value;
bool warning_event_updated = false;
if (warning_events != 0) {
warning_event_updated = true;
_filter_warning_event_changes++;
}
if (information_event_updated || warning_event_updated) {
estimator_event_flags_s event_flags{};
event_flags.timestamp_sample = timestamp;
event_flags.information_event_changes = _filter_information_event_changes;
event_flags.gps_checks_passed = _ekf.information_event_flags().gps_checks_passed;
event_flags.reset_vel_to_gps = _ekf.information_event_flags().reset_vel_to_gps;
......@@ -590,7 +500,6 @@ void EKF2::PublishEventFlags(const hrt_abstime &timestamp)
event_flags.starting_vision_vel_fusion = _ekf.information_event_flags().starting_vision_vel_fusion;
event_flags.starting_vision_yaw_fusion = _ekf.information_event_flags().starting_vision_yaw_fusion;
event_flags.yaw_aligned_to_imu_gps = _ekf.information_event_flags().yaw_aligned_to_imu_gps;
event_flags.warning_event_changes = _filter_warning_event_changes;
event_flags.gps_quality_poor = _ekf.warning_event_flags().gps_quality_poor;
event_flags.gps_fusion_timout = _ekf.warning_event_flags().gps_fusion_timout;
......@@ -603,62 +512,48 @@ void EKF2::PublishEventFlags(const hrt_abstime &timestamp)
event_flags.stopping_mag_use = _ekf.warning_event_flags().stopping_mag_use;
event_flags.vision_data_stopped = _ekf.warning_event_flags().vision_data_stopped;
event_flags.emergency_yaw_reset_mag_stopped = _ekf.warning_event_flags().emergency_yaw_reset_mag_stopped;
event_flags.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_event_flags_pub.publish(event_flags);
}
_ekf.clear_information_events();
_ekf.clear_warning_events();
}
void EKF2::PublishGlobalPosition(const hrt_abstime &timestamp)
{
if (_ekf.global_position_is_valid() && !_preflt_checker.hasFailed()) {
// only publish if position has changed by at least 1 mm (map_projection_reproject is relatively expensive)
const Vector3f position{_ekf.getPosition()};
if ((_last_local_position_for_gpos - position).longerThan(0.001f)) {
// generate and publish global position data
vehicle_global_position_s global_pos;
global_pos.timestamp_sample = timestamp;
// Position of local NED origin in GPS / WGS84 frame
map_projection_reproject(&_ekf.global_origin(), position(0), position(1), &global_pos.lat, &global_pos.lon);
float delta_xy[2];
_ekf.get_posNE_reset(delta_xy, &global_pos.lat_lon_reset_counter);
global_pos.alt = -position(2) + _ekf.getEkfGlobalOriginAltitude(); // Altitude AMSL in meters
global_pos.alt_ellipsoid = filter_altitude_ellipsoid(global_pos.alt);
// global altitude has opposite sign of local down position
float delta_z;
uint8_t z_reset_counter;
_ekf.get_posD_reset(&delta_z, &z_reset_counter);
global_pos.delta_alt = -delta_z;
_ekf.get_ekf_gpos_accuracy(&global_pos.eph, &global_pos.epv);
if (_ekf.isTerrainEstimateValid()) {
// Terrain altitude in m, WGS84
global_pos.terrain_alt = _ekf.getEkfGlobalOriginAltitude() - _ekf.getTerrainVertPos();
global_pos.terrain_alt_valid = true;
} else {
global_pos.terrain_alt = NAN;
global_pos.terrain_alt_valid = false;
}
global_pos.dead_reckoning = _ekf.inertial_dead_reckoning(); // True if this position is estimated through dead-reckoning
global_pos.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_global_position_pub.publish(global_pos);
_last_local_position_for_gpos = position;
}
}
}
void EKF2::PublishInnovations(const hrt_abstime &timestamp, const imuSample &imu)
{
// publish estimator innovation data
......@@ -678,10 +573,8 @@ void EKF2::PublishInnovations(const hrt_abstime &timestamp, const imuSample &imu
_ekf.getHaglInnov(innovations.hagl);
// Not yet supported
innovations.aux_vvel = NAN;
innovations.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_innovations_pub.publish(innovations);
// calculate noise filtered velocity innovations which are used for pre-flight checking
if (_standby) {
// TODO: move to run before publications
......@@ -689,11 +582,9 @@ void EKF2::PublishInnovations(const hrt_abstime &timestamp, const imuSample &imu
_preflt_checker.setUsingFlowAiding(_ekf.control_status_flags().opt_flow);
_preflt_checker.setUsingEvPosAiding(_ekf.control_status_flags().ev_pos);
_preflt_checker.setUsingEvVelAiding(_ekf.control_status_flags().ev_vel);
_preflt_checker.update(imu.delta_ang_dt, innovations);
}
}
void EKF2::PublishInnovationTestRatios(const hrt_abstime &timestamp)
{
// publish estimator innovation test ratio data
......@@ -714,11 +605,9 @@ void EKF2::PublishInnovationTestRatios(const hrt_abstime &timestamp)
_ekf.getHaglInnovRatio(test_ratios.hagl);
// Not yet supported
test_ratios.aux_vvel = NAN;
test_ratios.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_innovation_test_ratios_pub.publish(test_ratios);
}
void EKF2::PublishInnovationVariances(const hrt_abstime &timestamp)
{
// publish estimator innovation variance data
......@@ -738,44 +627,36 @@ void EKF2::PublishInnovationVariances(const hrt_abstime &timestamp)
_ekf.getHaglInnovVar(variances.hagl);
// Not yet supported
variances.aux_vvel = NAN;
variances.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_innovation_variances_pub.publish(variances);
}
void EKF2::PublishLocalPosition(const hrt_abstime &timestamp)
{
vehicle_local_position_s lpos;
// generate vehicle local position data
lpos.timestamp_sample = timestamp;
// Position of body origin in local NED frame
const Vector3f position{_ekf.getPosition()};
lpos.x = position(0);
lpos.y = position(1);
lpos.z = position(2);
// 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);
// vertical position time derivative (m/s)
lpos.z_deriv = _ekf.getVerticalPositionDerivative();
// Acceleration of body origin in local frame
const 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
if (_ekf.global_origin_valid()) {
lpos.ref_timestamp = _ekf.global_origin().timestamp;
......@@ -784,7 +665,6 @@ void EKF2::PublishLocalPosition(const hrt_abstime &timestamp)
lpos.ref_alt = _ekf.getEkfGlobalOriginAltitude(); // Reference point in MSL altitude meters
lpos.xy_global = true;
lpos.z_global = true;
} else {
lpos.ref_timestamp = 0;
lpos.ref_lat = static_cast<double>(NAN);
......@@ -793,147 +673,115 @@ void EKF2::PublishLocalPosition(const hrt_abstime &timestamp)
lpos.xy_global = false;
lpos.z_global = false;
}
Quatf delta_q_reset;
_ekf.get_quat_reset(&delta_q_reset(0), &lpos.heading_reset_counter);
lpos.heading = Eulerf(_ekf.getQuaternion()).psi();
lpos.delta_heading = Eulerf(delta_q_reset).psi();
// Distance to bottom surface (ground) in meters
// constrain the distance to ground to _rng_gnd_clearance
lpos.dist_bottom = math::max(_ekf.getTerrainVertPos() - lpos.z, _param_ekf2_min_rng.get());
lpos.dist_bottom_valid = _ekf.isTerrainEstimateValid();
lpos.dist_bottom_sensor_bitfield = _ekf.getTerrainEstimateSensorBitfield();
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) && _armed && lpos.dist_bottom_valid) {
// set ground effect flag if vehicle is closer than a specified distance to the ground
_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
}
_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;
}
// publish vehicle local position data
lpos.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_local_position_pub.publish(lpos);
}
void EKF2::PublishOdometry(const hrt_abstime &timestamp, const imuSample &imu)
{
// generate vehicle odometry data
vehicle_odometry_s odom;
odom.timestamp_sample = imu.time_us;
odom.local_frame = vehicle_odometry_s::LOCAL_FRAME_NED;
// Vehicle odometry position
const Vector3f position{_ekf.getPosition()};
odom.x = position(0);
odom.y = position(1);
odom.z = position(2);
// Vehicle odometry linear velocity
odom.velocity_frame = vehicle_odometry_s::LOCAL_FRAME_FRD;
const Vector3f velocity{_ekf.getVelocity()};
odom.vx = velocity(0);
odom.vy = velocity(1);
odom.vz = velocity(2);
// Vehicle odometry quaternion
_ekf.getQuaternion().copyTo(odom.q);
// Vehicle odometry angular rates
const Vector3f gyro_bias{_ekf.getGyroBias()};
const Vector3f rates{imu.delta_ang / imu.delta_ang_dt};
odom.rollspeed = rates(0) - gyro_bias(0);
odom.pitchspeed = rates(1) - gyro_bias(1);
odom.yawspeed = rates(2) - gyro_bias(2);
// get the covariance matrix size
static constexpr size_t POS_URT_SIZE = sizeof(odom.pose_covariance) / sizeof(odom.pose_covariance[0]);
static constexpr size_t VEL_URT_SIZE = sizeof(odom.velocity_covariance) / sizeof(odom.velocity_covariance[0]);
// Get covariances to vehicle odometry
float covariances[24];
_ekf.covariances_diagonal().copyTo(covariances);
// 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 odometry data
odom.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_odometry_pub.publish(odom);
}
void EKF2::PublishOdometryAligned(const hrt_abstime &timestamp, const vehicle_odometry_s &ev_odom)
{
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);
......@@ -942,7 +790,6 @@ void EKF2::PublishOdometryAligned(const hrt_abstime &timestamp, const vehicle_od
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);
......@@ -951,34 +798,26 @@ void EKF2::PublishOdometryAligned(const hrt_abstime &timestamp, const vehicle_od
break;
}
}
aligned_ev_odom.velocity_frame = vehicle_odometry_s::LOCAL_FRAME_NED;
// Compute orientation in EKF navigation frame
Quatf ev_quat_aligned = quat_ev2ekf * Quatf(ev_odom.q) ;
ev_quat_aligned.normalize();
ev_quat_aligned.copyTo(aligned_ev_odom.q);
quat_ev2ekf.copyTo(aligned_ev_odom.q_offset);
_estimator_visual_odometry_aligned_pub.publish(aligned_ev_odom);
}
void EKF2::PublishSensorBias(const hrt_abstime &timestamp)
{
// estimator_sensor_bias
estimator_sensor_bias_s bias{};
bias.timestamp_sample = timestamp;
const Vector3f gyro_bias{_ekf.getGyroBias()};
const Vector3f accel_bias{_ekf.getAccelBias()};
const Vector3f mag_bias{_mag_cal_last_bias};
// only publish on change
if ((gyro_bias - _last_gyro_bias_published).longerThan(0.001f)
|| (accel_bias - _last_accel_bias_published).longerThan(0.001f)
|| (mag_bias - _last_mag_bias_published).longerThan(0.001f)) {
// take device ids from sensor_selection_s if not using specific vehicle_imu_s
if (_device_id_gyro != 0) {
bias.gyro_device_id = _device_id_gyro;
......@@ -986,35 +825,28 @@ void EKF2::PublishSensorBias(const hrt_abstime &timestamp)
bias.gyro_bias_limit = math::radians(20.f); // 20 degrees/s see Ekf::constrainStates()
_ekf.getGyroBiasVariance().copyTo(bias.gyro_bias_variance);
bias.gyro_bias_valid = true;
_last_gyro_bias_published = gyro_bias;
}
if ((_device_id_accel != 0) && !(_param_ekf2_aid_mask.get() & MASK_INHIBIT_ACC_BIAS)) {
bias.accel_device_id = _device_id_accel;
accel_bias.copyTo(bias.accel_bias);
bias.accel_bias_limit = _params->acc_bias_lim;
_ekf.getAccelBiasVariance().copyTo(bias.accel_bias_variance);
bias.accel_bias_valid = !_ekf.fault_status_flags().bad_acc_bias;
_last_accel_bias_published = accel_bias;
}
if (_device_id_mag != 0) {
bias.mag_device_id = _device_id_mag;
mag_bias.copyTo(bias.mag_bias);
bias.mag_bias_limit = 0.5f; // 0.5 Gauss see Ekf::constrainStates()
_mag_cal_last_bias_variance.copyTo(bias.mag_bias_variance);
bias.mag_bias_valid = _mag_cal_available;
_last_mag_bias_published = mag_bias;
}
bias.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_sensor_bias_pub.publish(bias);
}
}
void EKF2::PublishStates(const hrt_abstime &timestamp)
{
// publish estimator states
......@@ -1026,91 +858,72 @@ void EKF2::PublishStates(const hrt_abstime &timestamp)
states.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_states_pub.publish(states);
}
void EKF2::PublishStatus(const hrt_abstime &timestamp)
{
estimator_status_s status{};
status.timestamp_sample = timestamp;
_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 = _ekf.control_status().value;
status.filter_fault_flags = _ekf.fault_status().value;
uint16_t innov_check_flags_temp = 0;
_ekf.get_innovation_test_status(innov_check_flags_temp, 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);
// Bit mismatch between ecl and Firmware, combine the 2 first bits to preserve msg definition
// TODO: legacy use only, those flags are also in estimator_status_flags
status.innovation_check_flags = (innov_check_flags_temp >> 1) | (innov_check_flags_temp & 0x1);
_ekf.get_ekf_lpos_accuracy(&status.pos_horiz_accuracy, &status.pos_vert_accuracy);
_ekf.get_ekf_soln_status(&status.solution_status_flags);
_ekf.getImuVibrationMetrics().copyTo(status.vibe);
// reset counters
status.reset_count_vel_ne = _ekf.state_reset_status().velNE_counter;
status.reset_count_vel_d = _ekf.state_reset_status().velD_counter;
status.reset_count_pos_ne = _ekf.state_reset_status().posNE_counter;
status.reset_count_pod_d = _ekf.state_reset_status().posD_counter;
status.reset_count_quat = _ekf.state_reset_status().quat_counter;
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 = _ekf.control_status_flags().mag_field_disturbed;
status.accel_device_id = _device_id_accel;
status.baro_device_id = _device_id_baro;
status.gyro_device_id = _device_id_gyro;
status.mag_device_id = _device_id_mag;
status.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_status_pub.publish(status);
}
void EKF2::PublishStatusFlags(const hrt_abstime &timestamp)
{
// publish at ~ 1 Hz (or immediately if filter control status or fault status changes)
bool update = (hrt_elapsed_time(&_last_status_flag_update) >= 1_s);
// filter control status
if (_ekf.control_status().value != _filter_control_status) {
update = true;
_filter_control_status = _ekf.control_status().value;
_filter_control_status_changes++;
}
// filter fault status
if (_ekf.fault_status().value != _filter_fault_status) {
update = true;
_filter_fault_status = _ekf.fault_status().value;
_filter_fault_status_changes++;
}
// innovation check fail status
if (_ekf.innov_check_fail_status().value != _innov_check_fail_status) {
update = true;
_innov_check_fail_status = _ekf.innov_check_fail_status().value;
_innov_check_fail_status_changes++;
}
if (update) {
estimator_status_flags_s status_flags{};
status_flags.timestamp_sample = timestamp;
status_flags.control_status_changes = _filter_control_status_changes;
status_flags.cs_tilt_align = _ekf.control_status_flags().tilt_align;
status_flags.cs_yaw_align = _ekf.control_status_flags().yaw_align;
......@@ -1139,7 +952,6 @@ void EKF2::PublishStatusFlags(const hrt_abstime &timestamp)
status_flags.cs_ev_vel = _ekf.control_status_flags().ev_vel;
status_flags.cs_synthetic_mag_z = _ekf.control_status_flags().synthetic_mag_z;
status_flags.cs_vehicle_at_rest = _ekf.control_status_flags().vehicle_at_rest;
status_flags.fault_status_changes = _filter_fault_status_changes;
status_flags.fs_bad_mag_x = _ekf.fault_status_flags().bad_mag_x;
status_flags.fs_bad_mag_y = _ekf.fault_status_flags().bad_mag_y;
......@@ -1159,7 +971,6 @@ void EKF2::PublishStatusFlags(const hrt_abstime &timestamp)
status_flags.fs_bad_acc_bias = _ekf.fault_status_flags().bad_acc_bias;
status_flags.fs_bad_acc_vertical = _ekf.fault_status_flags().bad_acc_vertical;
status_flags.fs_bad_acc_clipping = _ekf.fault_status_flags().bad_acc_clipping;
status_flags.innovation_fault_status_changes = _innov_check_fail_status_changes;
status_flags.reject_hor_vel = _ekf.innov_check_fail_status_flags().reject_hor_vel;
status_flags.reject_ver_vel = _ekf.innov_check_fail_status_flags().reject_ver_vel;
......@@ -1174,98 +985,76 @@ void EKF2::PublishStatusFlags(const hrt_abstime &timestamp)
status_flags.reject_hagl = _ekf.innov_check_fail_status_flags().reject_hagl;
status_flags.reject_optflow_x = _ekf.innov_check_fail_status_flags().reject_optflow_X;
status_flags.reject_optflow_y = _ekf.innov_check_fail_status_flags().reject_optflow_Y;
status_flags.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_status_flags_pub.publish(status_flags);
_last_status_flag_update = status_flags.timestamp;
}
}
void EKF2::PublishYawEstimatorStatus(const hrt_abstime &timestamp)
{
static_assert(sizeof(yaw_estimator_status_s::yaw) / sizeof(float) == N_MODELS_EKFGSF,
"yaw_estimator_status_s::yaw wrong size");
yaw_estimator_status_s yaw_est_test_data;
if (_ekf.getDataEKFGSF(&yaw_est_test_data.yaw_composite, &yaw_est_test_data.yaw_variance,
yaw_est_test_data.yaw,
yaw_est_test_data.innov_vn, yaw_est_test_data.innov_ve,
yaw_est_test_data.weight)) {
yaw_est_test_data.timestamp_sample = timestamp;
yaw_est_test_data.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_yaw_est_pub.publish(yaw_est_test_data);
}
}
void EKF2::PublishWindEstimate(const hrt_abstime &timestamp)
{
if (_ekf.get_wind_status()) {
// Publish wind estimate only if ekf declares them valid
wind_s wind{};
wind.timestamp_sample = timestamp;
const Vector2f wind_vel = _ekf.getWindVelocity();
const Vector2f wind_vel_var = _ekf.getWindVelocityVariance();
_ekf.getAirspeedInnov(wind.tas_innov);
_ekf.getAirspeedInnovVar(wind.tas_innov_var);
_ekf.getBetaInnov(wind.beta_innov);
_ekf.getBetaInnovVar(wind.beta_innov_var);
wind.windspeed_north = wind_vel(0);
wind.windspeed_east = wind_vel(1);
wind.variance_north = wind_vel_var(0);
wind.variance_east = wind_vel_var(1);
wind.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_wind_pub.publish(wind);
}
}
void EKF2::PublishOpticalFlowVel(const hrt_abstime &timestamp, const optical_flow_s &flow_sample)
{
estimator_optical_flow_vel_s flow_vel{};
flow_vel.timestamp_sample = flow_sample.timestamp;
_ekf.getFlowVelBody().copyTo(flow_vel.vel_body);
_ekf.getFlowVelNE().copyTo(flow_vel.vel_ne);
_ekf.getFlowUncompensated().copyTo(flow_vel.flow_uncompensated_integral);
_ekf.getFlowCompensated().copyTo(flow_vel.flow_compensated_integral);
_ekf.getFlowGyro().copyTo(flow_vel.gyro_rate_integral);
flow_vel.timestamp = _replay_mode ? timestamp : hrt_absolute_time();
_estimator_optical_flow_vel_pub.publish(flow_vel);
}
float EKF2::filter_altitude_ellipsoid(float amsl_hgt)
{
float height_diff = static_cast<float>(_gps_alttitude_ellipsoid) * 1e-3f - amsl_hgt;
if (_gps_alttitude_ellipsoid_previous_timestamp == 0) {
_wgs84_hgt_offset = height_diff;
_gps_alttitude_ellipsoid_previous_timestamp = _gps_time_usec;
} else if (_gps_time_usec != _gps_alttitude_ellipsoid_previous_timestamp) {
// apply a 10 second first order low pass filter to baro offset
float dt = 1e-6f * (_gps_time_usec - _gps_alttitude_ellipsoid_previous_timestamp);
_gps_alttitude_ellipsoid_previous_timestamp = _gps_time_usec;
float offset_rate_correction = 0.1f * (height_diff - _wgs84_hgt_offset);
_wgs84_hgt_offset += dt * constrain(offset_rate_correction, -0.1f, 0.1f);
}
return amsl_hgt + _wgs84_hgt_offset;
}
void EKF2::UpdateAirspeedSample(ekf2_timestamps_s &ekf2_timestamps)
{
// EKF airspeed sample
airspeed_s airspeed;
if (_airspeed_sub.update(&airspeed)) {
// The airspeed measurement received via the airspeed.msg topic has not been corrected
// for scale favtor errors and requires the ASPD_SCALE correction to be applied.
......@@ -1273,10 +1062,8 @@ void EKF2::UpdateAirspeedSample(ekf2_timestamps_s &ekf2_timestamps)
// was used instead, however this would introduce a potential circular dependency
// via the wind estimator that uses EKF velocity estimates.
const float true_airspeed_m_s = airspeed.true_airspeed_m_s * _airspeed_scale_factor;
// only set airspeed data if condition for airspeed fusion are met
if ((_param_ekf2_arsp_thr.get() > FLT_EPSILON) && (true_airspeed_m_s > _param_ekf2_arsp_thr.get())) {
airspeedSample airspeed_sample {
.time_us = airspeed.timestamp,
.true_airspeed = true_airspeed_m_s,
......@@ -1284,26 +1071,21 @@ void EKF2::UpdateAirspeedSample(ekf2_timestamps_s &ekf2_timestamps)
};
_ekf.setAirspeedData(airspeed_sample);
}
ekf2_timestamps.airspeed_timestamp_rel = (int16_t)((int64_t)airspeed.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
void EKF2::UpdateAuxVelSample(ekf2_timestamps_s &ekf2_timestamps)
{
// EKF auxillary velocity sample
// - use the landing target pose estimate as another source of velocity data
const unsigned last_generation = _landing_target_pose_sub.get_last_generation();
landing_target_pose_s landing_target_pose;
if (_landing_target_pose_sub.update(&landing_target_pose)) {
if (_landing_target_pose_sub.get_last_generation() != last_generation + 1) {
PX4_ERR("%d - landing_target_pose lost, generation %d -> %d", _instance, last_generation,
_landing_target_pose_sub.get_last_generation());
}
// 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
......@@ -1316,59 +1098,43 @@ void EKF2::UpdateAuxVelSample(ekf2_timestamps_s &ekf2_timestamps)
}
}
}
void EKF2::UpdateBaroSample(ekf2_timestamps_s &ekf2_timestamps)
{
// EKF baro sample
vehicle_air_data_s airdata;
if (_airdata_sub.update(&airdata)) {
_ekf.set_air_density(airdata.rho);
_ekf.setBaroData(baroSample{airdata.timestamp_sample, airdata.baro_alt_meter});
_device_id_baro = airdata.baro_device_id;
ekf2_timestamps.vehicle_air_data_timestamp_rel = (int16_t)((int64_t)airdata.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
bool EKF2::UpdateExtVisionSample(ekf2_timestamps_s &ekf2_timestamps, vehicle_odometry_s &ev_odom)
{
bool new_ev_odom = false;
const unsigned last_generation = _ev_odom_sub.get_last_generation();
// EKF external vision sample
if (_ev_odom_sub.update(&ev_odom)) {
if (_ev_odom_sub.get_last_generation() != last_generation + 1) {
PX4_ERR("%d - vehicle_visual_odometry lost, generation %d -> %d", _instance, last_generation,
_ev_odom_sub.get_last_generation());
}
if (_param_ekf2_aid_mask.get() & (MASK_USE_EVPOS | MASK_USE_EVYAW | MASK_USE_EVVEL)) {
extVisionSample ev_data{};
// if error estimates are unavailable, use parameter defined defaults
// 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 = velocity_frame_t::BODY_FRAME_FRD;
} else {
ev_data.vel_frame = velocity_frame_t::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])) {
......@@ -1378,20 +1144,16 @@ bool EKF2::UpdateExtVisionSample(ekf2_timestamps_s &ekf2_timestamps, vehicle_odo
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])
......@@ -1399,55 +1161,41 @@ bool EKF2::UpdateExtVisionSample(ekf2_timestamps_s &ekf2_timestamps, vehicle_odo
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 = 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);
new_ev_odom = true;
}
ekf2_timestamps.visual_odometry_timestamp_rel = (int16_t)((int64_t)ev_odom.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
return new_ev_odom;
}
bool EKF2::UpdateFlowSample(ekf2_timestamps_s &ekf2_timestamps, optical_flow_s &optical_flow)
{
bool new_optical_flow = false;
const unsigned last_generation = _optical_flow_sub.get_last_generation();
if (_optical_flow_sub.update(&optical_flow)) {
if (_optical_flow_sub.get_last_generation() != last_generation + 1) {
PX4_ERR("%d - optical_flow lost, generation %d -> %d", _instance, last_generation,
_optical_flow_sub.get_last_generation());
}
if (_param_ekf2_aid_mask.get() & MASK_USE_OF) {
flowSample flow {
.time_us = optical_flow.timestamp,
// NOTE: the EKF uses the reverse sign convention to the flow sensor. EKF assumes positive LOS rate
......@@ -1457,34 +1205,26 @@ bool EKF2::UpdateFlowSample(ekf2_timestamps_s &ekf2_timestamps, optical_flow_s &
.dt = 1e-6f * (float)optical_flow.integration_timespan,
.quality = optical_flow.quality,
};
if (PX4_ISFINITE(optical_flow.pixel_flow_y_integral) &&
PX4_ISFINITE(optical_flow.pixel_flow_x_integral) &&
flow.dt < 1) {
// 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);
_ekf.setOpticalFlowData(flow);
new_optical_flow = true;
}
}
ekf2_timestamps.optical_flow_timestamp_rel = (int16_t)((int64_t)optical_flow.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
return new_optical_flow;
}
void EKF2::UpdateGpsSample(ekf2_timestamps_s &ekf2_timestamps)
{
// EKF GPS message
if (_param_ekf2_aid_mask.get() & MASK_USE_GPS) {
vehicle_gps_position_s vehicle_gps_position;
if (_vehicle_gps_position_sub.update(&vehicle_gps_position)) {
gps_message gps_msg{
.time_usec = vehicle_gps_position.timestamp,
......@@ -1518,7 +1258,7 @@ void EKF2::UpdateGpsSample(ekf2_timestamps_s &ekf2_timestamps)
calculate_inclination_target();
print_target_gps();
//print_current_gps(gps_msg);
calculate_inclination_current(gps_msg);
check_inclination(calculate_inclination_current(gps_msg));
}
}
_gps_time_usec = gps_msg.time_usec;
......@@ -1526,6 +1266,19 @@ void EKF2::UpdateGpsSample(ekf2_timestamps_s &ekf2_timestamps)
}
}
}
void EKF2::check_inclination(double check)
{
double origin=inclination[int(mission.current_seq)-1];
std:: cout << "origin : "<<origin<<"\tcheck : "<<check<<std::endl;
if(origin-1<=check && check <= origin+1)
{
std::cout<<"*----Good moving----*"<<std::endl;
}
else
{
std::cout<<"*----Bad moving----*"<<std::endl;
}
}
void EKF2::print_current_gps(gps_message gps_msg)
{
std::cout<<"curre\t#"<<mission.current_seq<<"(lat/lon/alt)\t" << gps_msg.lat << "\t"<<gps_msg.lon<<"\t"<<gps_msg.alt<<std::endl;
......@@ -1534,7 +1287,7 @@ void EKF2::calculate_inclination_target()
{
double x,y;
inclination=new double[int(mission.count)];
for (size_t i = 0; i < mission.count-1; i++) {
for (int i = 0; i < int(mission.count)-1; i++) {
struct mission_item_s mission_item {};
struct mission_item_s next_mission_item {};
dm_read((dm_item_t)mission.dataman_id, i, &mission_item, sizeof(mission_item_s));
......@@ -1557,7 +1310,7 @@ void EKF2::calculate_inclination_target()
}*/
}
}
void EKF2::calculate_inclination_current(gps_message gps_msg)
double EKF2::calculate_inclination_current(gps_message gps_msg)
{
double x,y;
double check_inclination=0;
......@@ -1568,13 +1321,12 @@ void EKF2::calculate_inclination_current(gps_message gps_msg)
}
std::cout.setf(std::ios::fixed);
std::cout.precision(7);
std::cout << "[Mission] #"<<mission.current_seq<<"\t"<<check_inclination<<std::endl;
//std::cout << "[Mission] #"<<mission.current_seq<<"\t"<<check_inclination<<std::endl;
return check_inclination;
}
void EKF2::print_target_gps()
{
std::cout.unsetf(std::ios::fixed);
for (size_t i = 0; i < mission.count; i++) {
struct mission_item_s mission_item {};
if(int(mission.current_seq)==int(i)){
......@@ -1590,63 +1342,49 @@ void EKF2::UpdateMagSample(ekf2_timestamps_s &ekf2_timestamps)
{
const unsigned last_generation = _magnetometer_sub.get_last_generation();
vehicle_magnetometer_s magnetometer;
if (_magnetometer_sub.update(&magnetometer)) {
if (_magnetometer_sub.get_last_generation() != last_generation + 1) {
perf_count(_mag_missed_perf);
PX4_DEBUG("%d - vehicle_magnetometer lost, generation %d -> %d", _instance, last_generation,
_magnetometer_sub.get_last_generation());
}
bool reset = false;
// check if magnetometer has changed
if (magnetometer.device_id != _device_id_mag) {
if (_device_id_mag != 0) {
PX4_WARN("%d - mag sensor ID changed %d -> %d", _instance, _device_id_mag, magnetometer.device_id);
}
reset = true;
} else if (magnetometer.calibration_count > _mag_calibration_count) {
// existing calibration has changed, reset saved mag bias
PX4_DEBUG("%d - mag %d calibration updated, resetting bias", _instance, _device_id_mag);
reset = true;
}
if (reset) {
_ekf.resetMagBias();
_device_id_mag = magnetometer.device_id;
_mag_calibration_count = magnetometer.calibration_count;
// reset magnetometer bias learning
_mag_cal_total_time_us = 0;
_mag_cal_last_us = 0;
_mag_cal_available = false;
}
_ekf.setMagData(magSample{magnetometer.timestamp_sample, Vector3f{magnetometer.magnetometer_ga}});
ekf2_timestamps.vehicle_magnetometer_timestamp_rel = (int16_t)((int64_t)magnetometer.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
}
}
void EKF2::UpdateRangeSample(ekf2_timestamps_s &ekf2_timestamps)
{
if (!_distance_sensor_selected) {
// get subscription index of first downward-facing range sensor
uORB::SubscriptionMultiArray<distance_sensor_s> distance_sensor_subs{ORB_ID::distance_sensor};
for (unsigned i = 0; i < distance_sensor_subs.size(); i++) {
distance_sensor_s distance_sensor;
if (distance_sensor_subs[i].copy(&distance_sensor)) {
// only use the first instace which has the correct orientation
if ((hrt_elapsed_time(&distance_sensor.timestamp) < 100_ms)
&& (distance_sensor.orientation == distance_sensor_s::ROTATION_DOWNWARD_FACING)) {
if (_distance_sensor_sub.ChangeInstance(i)) {
PX4_INFO("%d - selected distance_sensor:%d", _instance, i);
_distance_sensor_selected = true;
......@@ -1655,21 +1393,16 @@ void EKF2::UpdateRangeSample(ekf2_timestamps_s &ekf2_timestamps)
}
}
}
// EKF range sample
const unsigned last_generation = _distance_sensor_sub.get_last_generation();
distance_sensor_s distance_sensor;
if (_distance_sensor_sub.update(&distance_sensor)) {
if (_distance_sensor_sub.get_last_generation() != last_generation + 1) {
PX4_ERR("%d - distance_sensor lost, generation %d -> %d", _instance, last_generation,
_distance_sensor_sub.get_last_generation());
}
ekf2_timestamps.distance_sensor_timestamp_rel = (int16_t)((int64_t)distance_sensor.timestamp / 100 -
(int64_t)ekf2_timestamps.timestamp / 100);
if (distance_sensor.orientation == distance_sensor_s::ROTATION_DOWNWARD_FACING) {
rangeSample range_sample {
.time_us = distance_sensor.timestamp,
......@@ -1677,29 +1410,23 @@ void EKF2::UpdateRangeSample(ekf2_timestamps_s &ekf2_timestamps)
.quality = distance_sensor.signal_quality,
};
_ekf.setRangeData(range_sample);
// Save sensor limits reported by the rangefinder
_ekf.set_rangefinder_limits(distance_sensor.min_distance, distance_sensor.max_distance);
_last_range_sensor_update = distance_sensor.timestamp;
return;
}
}
if (hrt_elapsed_time(&_last_range_sensor_update) > 1_s) {
_distance_sensor_selected = false;
}
}
void EKF2::UpdateMagCalibration(const hrt_abstime &timestamp)
{
// Check if conditions are OK for learning of magnetometer bias values
// the EKF is operating in the correct mode and there are no filter faults
if (_ekf.control_status_flags().in_air && _ekf.control_status_flags().mag_3D && (_ekf.fault_status().value == 0)) {
if (_mag_cal_last_us != 0) {
_mag_cal_total_time_us += timestamp - _mag_cal_last_us;
// Start checking mag bias estimates when we have accumulated sufficient calibration time
if (_mag_cal_total_time_us > 30_s) {
_mag_cal_last_bias = _ekf.getMagBias();
......@@ -1707,30 +1434,24 @@ void EKF2::UpdateMagCalibration(const hrt_abstime &timestamp)
_mag_cal_available = true;
}
}
_mag_cal_last_us = timestamp;
} else {
// conditions are NOT OK for learning magnetometer bias, reset timestamp
// but keep the accumulated calibration time
_mag_cal_last_us = 0;
if (_ekf.fault_status().value != 0) {
// if a filter fault has occurred, assume previous learning was invalid and do not
// count it towards total learning time.
_mag_cal_total_time_us = 0;
}
}
if (!_armed) {
// update stored declination value
if (!_mag_decl_saved) {
float declination_deg;
if (_ekf.get_mag_decl_deg(&declination_deg)) {
_param_ekf2_mag_decl.set(declination_deg);
_mag_decl_saved = true;
if (!_multi_mode) {
_param_ekf2_mag_decl.commit_no_notification();
}
......@@ -1738,50 +1459,39 @@ void EKF2::UpdateMagCalibration(const hrt_abstime &timestamp)
}
}
}
int EKF2::custom_command(int argc, char *argv[])
{
return print_usage("unknown command");
}
int EKF2::task_spawn(int argc, char *argv[])
{
bool success = false;
bool replay_mode = false;
if (argc > 1 && !strcmp(argv[1], "-r")) {
PX4_INFO("replay mode enabled");
replay_mode = true;
}
#if !defined(CONSTRAINED_FLASH)
bool multi_mode = false;
int32_t imu_instances = 0;
int32_t mag_instances = 0;
int32_t sens_imu_mode = 1;
param_get(param_find("SENS_IMU_MODE"), &sens_imu_mode);
if (sens_imu_mode == 0) {
// ekf selector requires SENS_IMU_MODE = 0
multi_mode = true;
// IMUs (1 - 4 supported)
param_get(param_find("EKF2_MULTI_IMU"), &imu_instances);
if (imu_instances < 1 || imu_instances > 4) {
const int32_t imu_instances_limited = math::constrain(imu_instances, 1, 4);
PX4_WARN("EKF2_MULTI_IMU limited %d -> %d", imu_instances, imu_instances_limited);
param_set_no_notification(param_find("EKF2_MULTI_IMU"), &imu_instances_limited);
imu_instances = imu_instances_limited;
}
int32_t sens_mag_mode = 1;
param_get(param_find("SENS_MAG_MODE"), &sens_mag_mode);
if (sens_mag_mode == 0) {
param_get(param_find("EKF2_MULTI_MAG"), &mag_instances);
// Mags (1 - 4 supported)
if (mag_instances < 1 || mag_instances > 4) {
const int32_t mag_instances_limited = math::constrain(mag_instances, 1, 4);
......@@ -1789,167 +1499,126 @@ int EKF2::task_spawn(int argc, char *argv[])
param_set_no_notification(param_find("EKF2_MULTI_MAG"), &mag_instances_limited);
mag_instances = mag_instances_limited;
}
} else {
mag_instances = 1;
}
}
if (multi_mode) {
// Start EKF2Selector if it's not already running
if (_ekf2_selector.load() == nullptr) {
EKF2Selector *inst = new EKF2Selector();
if (inst) {
_ekf2_selector.store(inst);
} else {
PX4_ERR("Failed to create EKF2 selector");
return PX4_ERROR;
}
}
const hrt_abstime time_started = hrt_absolute_time();
const int multi_instances = math::min(imu_instances * mag_instances, (int)EKF2_MAX_INSTANCES);
int multi_instances_allocated = 0;
// allocate EKF2 instances until all found or arming
uORB::SubscriptionData<vehicle_status_s> vehicle_status_sub{ORB_ID(vehicle_status)};
bool ekf2_instance_created[4][4] {}; // IMUs * mags
while ((multi_instances_allocated < multi_instances)
&& (vehicle_status_sub.get().arming_state != vehicle_status_s::ARMING_STATE_ARMED)
&& ((hrt_elapsed_time(&time_started) < 30_s)
|| (vehicle_status_sub.get().hil_state == vehicle_status_s::HIL_STATE_ON))) {
vehicle_status_sub.update();
for (uint8_t mag = 0; mag < mag_instances; mag++) {
uORB::SubscriptionData<vehicle_magnetometer_s> vehicle_mag_sub{ORB_ID(vehicle_magnetometer), mag};
for (uint8_t imu = 0; imu < imu_instances; imu++) {
uORB::SubscriptionData<vehicle_imu_s> vehicle_imu_sub{ORB_ID(vehicle_imu), imu};
vehicle_mag_sub.update();
// Mag & IMU data must be valid, first mag can be ignored initially
if ((vehicle_mag_sub.get().device_id != 0 || mag == 0)
&& (vehicle_imu_sub.get().accel_device_id != 0)
&& (vehicle_imu_sub.get().gyro_device_id != 0)) {
if (!ekf2_instance_created[imu][mag]) {
EKF2 *ekf2_inst = new EKF2(true, px4::ins_instance_to_wq(imu), false);
if (ekf2_inst && ekf2_inst->multi_init(imu, mag)) {
int actual_instance = ekf2_inst->instance(); // match uORB instance numbering
if ((actual_instance >= 0) && (_objects[actual_instance].load() == nullptr)) {
_objects[actual_instance].store(ekf2_inst);
success = true;
multi_instances_allocated++;
ekf2_instance_created[imu][mag] = true;
if (actual_instance == 0) {
// force selector to run immediately if first instance started
_ekf2_selector.load()->ScheduleNow();
}
PX4_INFO("starting instance %d, IMU:%d (%d), MAG:%d (%d)", actual_instance,
imu, vehicle_imu_sub.get().accel_device_id,
mag, vehicle_mag_sub.get().device_id);
// sleep briefly before starting more instances
px4_usleep(10000);
} else {
PX4_ERR("instance numbering problem instance: %d", actual_instance);
delete ekf2_inst;
break;
}
} else {
PX4_ERR("alloc and init failed imu: %d mag:%d", imu, mag);
px4_usleep(1000000);
break;
}
}
} else {
px4_usleep(50000); // give the sensors extra time to start
continue;
}
}
}
if (multi_instances_allocated < multi_instances) {
px4_usleep(100000);
}
}
}
#endif // !CONSTRAINED_FLASH
else {
// otherwise launch regular
EKF2 *ekf2_inst = new EKF2(false, px4::wq_configurations::INS0, replay_mode);
if (ekf2_inst) {
_objects[0].store(ekf2_inst);
ekf2_inst->ScheduleNow();
success = true;
}
}
return success ? PX4_OK : 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[])
{
if (argc <= 1 || strcmp(argv[1], "-h") == 0) {
return EKF2::print_usage();
}
if (strcmp(argv[1], "start") == 0) {
int ret = 0;
EKF2::lock_module();
ret = EKF2::task_spawn(argc - 1, argv + 1);
if (ret < 0) {
PX4_ERR("start failed (%i)", ret);
}
EKF2::unlock_module();
return ret;
} else if (strcmp(argv[1], "status") == 0) {
if (EKF2::trylock_module()) {
#if !defined(CONSTRAINED_FLASH)
......@@ -1957,32 +1626,24 @@ extern "C" __EXPORT int ekf2_main(int argc, char *argv[])
_ekf2_selector.load()->PrintStatus();
}
#endif // !CONSTRAINED_FLASH
for (int i = 0; i < EKF2_MAX_INSTANCES; i++) {
if (_objects[i].load()) {
PX4_INFO_RAW("\n");
_objects[i].load()->print_status();
}
}
EKF2::unlock_module();
} else {
PX4_WARN("module locked, try again later");
}
return 0;
} else if (strcmp(argv[1], "stop") == 0) {
EKF2::lock_module();
if (argc > 2) {
int instance = atoi(argv[2]);
if (instance >= 0 && instance < EKF2_MAX_INSTANCES) {
PX4_INFO("stopping instance %d", instance);
EKF2 *inst = _objects[instance].load();
if (inst) {
inst->request_stop();
px4_usleep(20000); // 20 ms
......@@ -1992,11 +1653,9 @@ extern "C" __EXPORT int ekf2_main(int argc, char *argv[])
} else {
PX4_ERR("invalid instance %d", instance);
}
} else {
// otherwise stop everything
bool was_running = false;
#if !defined(CONSTRAINED_FLASH)
if (_ekf2_selector.load()) {
PX4_INFO("stopping ekf2 selector");
......@@ -2006,10 +1665,8 @@ extern "C" __EXPORT int ekf2_main(int argc, char *argv[])
was_running = true;
}
#endif // !CONSTRAINED_FLASH
for (int i = 0; i < EKF2_MAX_INSTANCES; i++) {
EKF2 *inst = _objects[i].load();
if (inst) {
PX4_INFO("stopping ekf2 instance %d", i);
was_running = true;
......@@ -2019,19 +1676,15 @@ extern "C" __EXPORT int ekf2_main(int argc, char *argv[])
_objects[i].store(nullptr);
}
}
if (!was_running) {
PX4_WARN("not running");
}
}
EKF2::unlock_module();
return PX4_OK;
}
EKF2::lock_module(); // Lock here, as the method could access _object.
int ret = EKF2::custom_command(argc - 1, argv + 1);
EKF2::unlock_module();
return ret;
}
......
......@@ -30,18 +30,14 @@
* POSSIBILITY OF SUCH DAMAGE.
*
****************************************************************************/
/**
* @file EKF2.cpp
* Implementation of the attitude and position estimator.
*
* @author Roman Bapst
*/
#pragma once
#include "EKF2Selector.hpp"
#include <float.h>
#include <dataman/dataman.h>
#include <containers/LockGuard.hpp>
......@@ -89,44 +85,30 @@
#include <uORB/topics/vehicle_status.h>
#include <uORB/topics/wind.h>
#include <uORB/topics/yaw_estimator_status.h>
#include "Utility/PreFlightChecker.hpp"
extern pthread_mutex_t ekf2_module_mutex;
class EKF2 final : public ModuleParams, public px4::ScheduledWorkItem
{
public:
EKF2() = delete;
EKF2(bool multi_mode, const px4::wq_config_t &config, bool replay_mode);
~EKF2() override;
/** @see ModuleBase */
static int task_spawn(int argc, char *argv[]);
/** @see ModuleBase */
static int custom_command(int argc, char *argv[]);
/** @see ModuleBase */
static int print_usage(const char *reason = nullptr);
int print_status();
bool should_exit() const { return _task_should_exit.load(); }
void request_stop() { _task_should_exit.store(true); }
static void lock_module() { pthread_mutex_lock(&ekf2_module_mutex); }
static bool trylock_module() { return (pthread_mutex_trylock(&ekf2_module_mutex) == 0); }
static void unlock_module() { pthread_mutex_unlock(&ekf2_module_mutex); }
bool multi_init(int imu, int mag);
int instance() const { return _instance; }
private:
void Run() override;
void PublishAttitude(const hrt_abstime &timestamp);
void PublishEkfDriftMetrics(const hrt_abstime &timestamp);
void PublishEventFlags(const hrt_abstime &timestamp);
......@@ -144,7 +126,6 @@ private:
void PublishStatusFlags(const hrt_abstime &timestamp);
void PublishWindEstimate(const hrt_abstime &timestamp);
void PublishYawEstimatorStatus(const hrt_abstime &timestamp);
void UpdateAirspeedSample(ekf2_timestamps_s &ekf2_timestamps);
void UpdateAuxVelSample(ekf2_timestamps_s &ekf2_timestamps);
void UpdateBaroSample(ekf2_timestamps_s &ekf2_timestamps);
......@@ -153,12 +134,12 @@ private:
void UpdateGpsSample(ekf2_timestamps_s &ekf2_timestamps);
void UpdateMagSample(ekf2_timestamps_s &ekf2_timestamps);
void UpdateRangeSample(ekf2_timestamps_s &ekf2_timestamps);
void UpdateMagCalibration(const hrt_abstime &timestamp);
void print_target_gps();
void print_current_gps(gps_message gps_msg);
void calculate_inclination_target();
void calculate_inclination_current(gps_message gps_msg);
double calculate_inclination_current(gps_message gps_msg);
void check_inclination(double check);
double* inclination=nullptr;
int32_t target_lat,target_lon,target_alt;
mission_s mission;
......@@ -166,60 +147,44 @@ private:
* Calculate filtered WGS84 height from estimated AMSL height
*/
float filter_altitude_ellipsoid(float amsl_hgt);
static constexpr float sq(float x) { return x * x; };
const bool _replay_mode{false}; ///< true when we use replay data from a log
const bool _multi_mode;
int _instance{0};
px4::atomic_bool _task_should_exit{false};
// time slip monitoring
uint64_t _integrated_time_us = 0; ///< integral of gyro delta time from start (uSec)
uint64_t _start_time_us = 0; ///< system time at EKF start (uSec)
int64_t _last_time_slip_us = 0; ///< Last time slip (uSec)
perf_counter_t _ecl_ekf_update_perf{perf_alloc(PC_ELAPSED, MODULE_NAME": ECL update")};
perf_counter_t _ecl_ekf_update_full_perf{perf_alloc(PC_ELAPSED, MODULE_NAME": ECL full update")};
perf_counter_t _imu_missed_perf{perf_alloc(PC_COUNT, MODULE_NAME": IMU message missed")};
perf_counter_t _mag_missed_perf{perf_alloc(PC_COUNT, MODULE_NAME": mag message missed")};
// Used to check, save and use learned magnetometer biases
hrt_abstime _mag_cal_last_us{0}; ///< last time the EKF was operating a mode that estimates magnetomer biases (uSec)
hrt_abstime _mag_cal_total_time_us{0}; ///< accumulated calibration time since the last save
Vector3f _mag_cal_last_bias{}; ///< last valid XYZ magnetometer bias estimates (Gauss)
Vector3f _mag_cal_last_bias_variance{}; ///< variances for the last valid magnetometer XYZ bias estimates (Gauss**2)
bool _mag_cal_available{false}; ///< true when an unsaved valid calibration for the XYZ magnetometer bias is available
// Used to control saving of mag declination to be used on next startup
bool _mag_decl_saved = false; ///< true when the magnetic declination has been saved
bool _had_valid_terrain{false}; ///< true if at any time there was a valid terrain estimate
uint64_t _gps_time_usec{0};
int32_t _gps_alttitude_ellipsoid{0}; ///< altitude in 1E-3 meters (millimeters) above ellipsoid
uint64_t _gps_alttitude_ellipsoid_previous_timestamp{0}; ///< storage for previous timestamp to compute dt
float _wgs84_hgt_offset = 0; ///< height offset between AMSL and WGS84
uint8_t _imu_calibration_count{0};
uint8_t _mag_calibration_count{0};
uint32_t _device_id_accel{0};
uint32_t _device_id_baro{0};
uint32_t _device_id_gyro{0};
uint32_t _device_id_mag{0};
Vector3f _last_local_position_for_gpos{};
Vector3f _last_accel_bias_published{};
Vector3f _last_gyro_bias_published{};
Vector3f _last_mag_bias_published{};
float _airspeed_scale_factor{1.0f}; ///< scale factor correction applied to airspeed measurements
uORB::SubscriptionInterval _parameter_update_sub{ORB_ID(parameter_update), 1_s};
uORB::Subscription _airdata_sub{ORB_ID(vehicle_air_data)};
uORB::Subscription _airspeed_sub{ORB_ID(airspeed)};
uORB::Subscription _distance_sensor_sub{ORB_ID(distance_sensor)};
......@@ -232,29 +197,22 @@ private:
uORB::Subscription _vehicle_command_sub{ORB_ID(vehicle_command)};
uORB::Subscription _vehicle_gps_position_sub{ORB_ID(vehicle_gps_position)};
uORB::Subscription _vehicle_land_detected_sub{ORB_ID(vehicle_land_detected)};
uORB::SubscriptionCallbackWorkItem _sensor_combined_sub{this, ORB_ID(sensor_combined)};
uORB::SubscriptionCallbackWorkItem _vehicle_imu_sub{this, ORB_ID(vehicle_imu)};
bool _callback_registered{false};
bool _distance_sensor_selected{false}; // because we can have several distance sensor instances with different orientations
bool _armed{false};
bool _standby{false}; // standby arming state
hrt_abstime _last_status_flag_update{0};
hrt_abstime _last_range_sensor_update{0};
uint32_t _filter_control_status{0};
uint32_t _filter_fault_status{0};
uint32_t _innov_check_fail_status{0};
uint32_t _filter_control_status_changes{0};
uint32_t _filter_fault_status_changes{0};
uint32_t _innov_check_fail_status_changes{0};
uint32_t _filter_warning_event_changes{0};
uint32_t _filter_information_event_changes{0};
uORB::PublicationMulti<ekf2_timestamps_s> _ekf2_timestamps_pub{ORB_ID(ekf2_timestamps)};
uORB::PublicationMulti<ekf_gps_drift_s> _ekf_gps_drift_pub{ORB_ID(ekf_gps_drift)};
uORB::PublicationMulti<estimator_innovations_s> _estimator_innovation_test_ratios_pub{ORB_ID(estimator_innovation_test_ratios)};
......@@ -268,21 +226,15 @@ private:
uORB::PublicationMulti<estimator_event_flags_s> _estimator_event_flags_pub{ORB_ID(estimator_event_flags)};
uORB::PublicationMulti<vehicle_odometry_s> _estimator_visual_odometry_aligned_pub{ORB_ID(estimator_visual_odometry_aligned)};
uORB::PublicationMulti<yaw_estimator_status_s> _yaw_est_pub{ORB_ID(yaw_estimator_status)};
// publications with topic dependent on multi-mode
uORB::PublicationMulti<vehicle_attitude_s> _attitude_pub;
uORB::PublicationMulti<vehicle_local_position_s> _local_position_pub;
uORB::PublicationMulti<vehicle_global_position_s> _global_position_pub;
uORB::PublicationMulti<vehicle_odometry_s> _odometry_pub;
uORB::PublicationMulti<wind_s> _wind_pub;
PreFlightChecker _preflt_checker;
Ekf _ekf;
parameters *_params; ///< pointer to ekf parameter struct (located in _ekf class instance)
DEFINE_PARAMETERS(
(ParamExtInt<px4::params::EKF2_MIN_OBS_DT>)
_param_ekf2_min_obs_dt, ///< Maximum time delay of any sensor used to increase buffer length to handle large timing jitter (mSec)
......@@ -302,12 +254,10 @@ private:
_param_ekf2_ev_delay, ///< off-board vision measurement delay relative to the IMU (mSec)
(ParamExtFloat<px4::params::EKF2_AVEL_DELAY>)
_param_ekf2_avel_delay, ///< auxillary velocity measurement delay relative to the IMU (mSec)
(ParamExtFloat<px4::params::EKF2_GYR_NOISE>)
_param_ekf2_gyr_noise, ///< IMU angular rate noise used for covariance prediction (rad/sec)
(ParamExtFloat<px4::params::EKF2_ACC_NOISE>)
_param_ekf2_acc_noise, ///< IMU acceleration noise use for covariance prediction (m/sec**2)
// process noise
(ParamExtFloat<px4::params::EKF2_GYR_B_NOISE>)
_param_ekf2_gyr_b_noise, ///< process noise for IMU rate gyro bias prediction (rad/sec**2)
......@@ -322,7 +272,6 @@ private:
(ParamExtFloat<px4::params::EKF2_TERR_NOISE>) _param_ekf2_terr_noise, ///< process noise for terrain offset (m/sec)
(ParamExtFloat<px4::params::EKF2_TERR_GRAD>)
_param_ekf2_terr_grad, ///< gradient of terrain used to estimate process noise due to changing position (m/m)
(ParamExtFloat<px4::params::EKF2_GPS_V_NOISE>)
_param_ekf2_gps_v_noise, ///< minimum allowed observation noise for gps velocity fusion (m/sec)
(ParamExtFloat<px4::params::EKF2_GPS_P_NOISE>)
......@@ -343,7 +292,6 @@ private:
_param_ekf2_gps_v_gate, ///< GPS velocity innovation consistency gate size (STD)
(ParamExtFloat<px4::params::EKF2_TAS_GATE>)
_param_ekf2_tas_gate, ///< True Airspeed innovation consistency gate size (STD)
// control of magnetometer fusion
(ParamExtFloat<px4::params::EKF2_HEAD_NOISE>)
_param_ekf2_head_noise, ///< measurement noise used for simple heading fusion (rad)
......@@ -367,7 +315,6 @@ private:
_param_ekf2_mag_acclim, ///< integer used to specify the type of magnetometer fusion used
(ParamExtFloat<px4::params::EKF2_MAG_YAWLIM>)
_param_ekf2_mag_yawlim, ///< yaw rate threshold used by mode select logic (rad/sec)
(ParamExtInt<px4::params::EKF2_GPS_CHECK>)
_param_ekf2_gps_check, ///< bitmask used to control which GPS quality checks are used
(ParamExtFloat<px4::params::EKF2_REQ_EPH>) _param_ekf2_req_eph, ///< maximum acceptable horiz position error (m)
......@@ -379,7 +326,6 @@ private:
(ParamExtFloat<px4::params::EKF2_REQ_HDRIFT>)
_param_ekf2_req_hdrift, ///< maximum acceptable horizontal drift speed (m/s)
(ParamExtFloat<px4::params::EKF2_REQ_VDRIFT>) _param_ekf2_req_vdrift, ///< maximum acceptable vertical drift speed (m/s)
// measurement source control
(ParamExtInt<px4::params::EKF2_AID_MASK>)
_param_ekf2_aid_mask, ///< bitmasked integer that selects which of the GPS and optical flow aiding sources will be used
......@@ -388,7 +334,6 @@ private:
_param_ekf2_terr_mask, ///< bitmasked integer that selects which of range finder and optical flow aiding sources will be used for terrain estimation
(ParamExtInt<px4::params::EKF2_NOAID_TOUT>)
_param_ekf2_noaid_tout, ///< maximum lapsed time from last fusion of measurements that constrain drift before the EKF will report the horizontal nav solution invalid (uSec)
// range finder fusion
(ParamExtFloat<px4::params::EKF2_RNG_NOISE>)
_param_ekf2_rng_noise, ///< observation noise for range finder measurements (m)
......@@ -407,7 +352,6 @@ private:
_param_ekf2_rng_a_igate, ///< gate size used for innovation consistency checks for range aid fusion (STD)
(ParamExtFloat<px4::params::EKF2_RNG_QLTY_T>)
_param_ekf2_rng_qlty_t, ///< Minimum duration during which the reported range finder signal quality needs to be non-zero in order to be declared valid (s)
// vision estimate fusion
(ParamInt<px4::params::EKF2_EV_NOISE_MD>)
_param_ekf2_ev_noise_md, ///< determine source of vision observation noise
......@@ -421,7 +365,6 @@ private:
_param_ekf2_evv_gate, ///< external vision velocity innovation consistency gate size (STD)
(ParamExtFloat<px4::params::EKF2_EVP_GATE>)
_param_ekf2_evp_gate, ///< external vision position innovation consistency gate size (STD)
// optical flow fusion
(ParamExtFloat<px4::params::EKF2_OF_N_MIN>)
_param_ekf2_of_n_min, ///< best quality observation noise for optical flow LOS rate measurements (rad/sec)
......@@ -431,7 +374,6 @@ private:
_param_ekf2_of_qmin, ///< minimum acceptable quality integer from the flow sensor
(ParamExtFloat<px4::params::EKF2_OF_GATE>)
_param_ekf2_of_gate, ///< optical flow fusion innovation consistency gate size (STD)
// sensor positions in body frame
(ParamExtFloat<px4::params::EKF2_IMU_POS_X>) _param_ekf2_imu_pos_x, ///< X position of IMU in body frame (m)
(ParamExtFloat<px4::params::EKF2_IMU_POS_Y>) _param_ekf2_imu_pos_y, ///< Y position of IMU in body frame (m)
......@@ -454,19 +396,16 @@ private:
_param_ekf2_ev_pos_y, ///< Y position of VI sensor focal point in body frame (m)
(ParamExtFloat<px4::params::EKF2_EV_POS_Z>)
_param_ekf2_ev_pos_z, ///< Z position of VI sensor focal point in body frame (m)
// control of airspeed and sideslip fusion
(ParamFloat<px4::params::EKF2_ARSP_THR>)
_param_ekf2_arsp_thr, ///< A value of zero will disabled airspeed fusion. Any positive value sets the minimum airspeed which will be used (m/sec)
(ParamInt<px4::params::EKF2_FUSE_BETA>)
_param_ekf2_fuse_beta, ///< Controls synthetic sideslip fusion, 0 disables, 1 enables
// output predictor filter time constants
(ParamExtFloat<px4::params::EKF2_TAU_VEL>)
_param_ekf2_tau_vel, ///< time constant used by the output velocity complementary filter (sec)
(ParamExtFloat<px4::params::EKF2_TAU_POS>)
_param_ekf2_tau_pos, ///< time constant used by the output position complementary filter (sec)
// IMU switch on bias parameters
(ParamExtFloat<px4::params::EKF2_GBIAS_INIT>)
_param_ekf2_gbias_init, ///< 1-sigma gyro bias uncertainty at switch on (rad/sec)
......@@ -474,7 +413,6 @@ private:
_param_ekf2_abias_init, ///< 1-sigma accelerometer bias uncertainty at switch on (m/sec**2)
(ParamExtFloat<px4::params::EKF2_ANGERR_INIT>)
_param_ekf2_angerr_init, ///< 1-sigma tilt error after initial alignment using gravity vector (rad)
// EKF accel bias learning control
(ParamExtFloat<px4::params::EKF2_ABL_LIM>) _param_ekf2_abl_lim, ///< Accelerometer bias learning limit (m/s**2)
(ParamExtFloat<px4::params::EKF2_ABL_ACCLIM>)
......@@ -483,13 +421,11 @@ private:
_param_ekf2_abl_gyrlim, ///< Maximum IMU gyro angular rate magnitude that allows IMU bias learning (m/s**2)
(ParamExtFloat<px4::params::EKF2_ABL_TAU>)
_param_ekf2_abl_tau, ///< Time constant used to inhibit IMU delta velocity bias learning (sec)
// Multi-rotor drag specific force fusion
(ParamExtFloat<px4::params::EKF2_DRAG_NOISE>)
_param_ekf2_drag_noise, ///< observation noise variance for drag specific force measurements (m/sec**2)**2
(ParamExtFloat<px4::params::EKF2_BCOEF_X>) _param_ekf2_bcoef_x, ///< ballistic coefficient along the X-axis (kg/m**2)
(ParamExtFloat<px4::params::EKF2_BCOEF_Y>) _param_ekf2_bcoef_y, ///< ballistic coefficient along the Y-axis (kg/m**2)
// Corrections for static pressure position error where Ps_error = Ps_meas - Ps_truth
// Coef = Ps_error / Pdynamic, where Pdynamic = 1/2 * density * TAS**2
(ParamExtFloat<px4::params::EKF2_ASPD_MAX>)
......@@ -504,19 +440,15 @@ private:
_param_ekf2_pcoef_yn, ///< static pressure position error coefficient along the negative Y body axis
(ParamExtFloat<px4::params::EKF2_PCOEF_Z>)
_param_ekf2_pcoef_z, ///< static pressure position error coefficient along the Z body axis
// Test used to determine if the vehicle is static or moving
(ParamExtFloat<px4::params::EKF2_MOVE_TEST>)
_param_ekf2_move_test, ///< scaling applied to IMU data thresholds used to determine if the vehicle is static or moving.
(ParamFloat<px4::params::EKF2_REQ_GPS_H>) _param_ekf2_req_gps_h, ///< Required GPS health time
(ParamExtInt<px4::params::EKF2_MAG_CHECK>) _param_ekf2_mag_check, ///< Mag field strength check
(ParamExtInt<px4::params::EKF2_SYNT_MAG_Z>)
_param_ekf2_synthetic_mag_z, ///< Enables the use of a synthetic value for the Z axis of the magnetometer calculated from the 3D magnetic field vector at the location of the drone.
// Used by EKF-GSF experimental yaw estimator
(ParamExtFloat<px4::params::EKF2_GSF_TAS>)
_param_ekf2_gsf_tas_default ///< default value of true airspeed assumed during fixed wing operation
)
};
......