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ekf2: refactor output predictor to class
- refactor all EKF backend output predictor pieces into new OutputPredictor class - output states are now calculated immediately with new high rate IMU rather than after EKF update - IMU delayed sample is passed as around as control data to avoid storing an extra copy and make the requirement clear
This commit is contained in:
Daniel Agar
+39
-267
@@ -61,13 +61,6 @@ void Ekf::reset()
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_state.wind_vel.setZero();
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_state.quat_nominal.setIdentity();
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// TODO: who resets the output buffer content?
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_output_new.vel.setZero();
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_output_new.pos.setZero();
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_output_new.quat_nominal.setIdentity();
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_delta_angle_corr.setZero();
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_range_sensor.setPitchOffset(_params.rng_sens_pitch);
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_range_sensor.setCosMaxTilt(_params.range_cos_max_tilt);
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_range_sensor.setQualityHysteresis(_params.range_valid_quality_s);
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@@ -86,12 +79,12 @@ void Ekf::reset()
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_prev_dvel_bias_var.zero();
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resetGpsDriftCheckFilters();
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_output_predictor.reset();
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}
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bool Ekf::update()
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{
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bool updated = false;
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if (!_filter_initialised) {
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_filter_initialised = initialiseFilter();
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@@ -102,24 +95,29 @@ bool Ekf::update()
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// Only run the filter if IMU data in the buffer has been updated
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if (_imu_updated) {
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_imu_updated = false;
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// get the oldest IMU data from the buffer
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// TODO: explicitly pop at desired time horizon
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const imuSample imu_sample_delayed = _imu_buffer.get_oldest();
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// perform state and covariance prediction for the main filter
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predictCovariance();
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predictState();
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predictCovariance(imu_sample_delayed);
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predictState(imu_sample_delayed);
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// control fusion of observation data
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controlFusionModes();
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controlFusionModes(imu_sample_delayed);
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// run a separate filter for terrain estimation
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runTerrainEstimator();
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runTerrainEstimator(imu_sample_delayed);
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updated = true;
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_output_predictor.correctOutputStates(imu_sample_delayed.time_us, getGyroBias(), getAccelBias(),
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_state.quat_nominal, _state.vel, _state.pos);
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return true;
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}
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// the output observer always runs
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// Use full rate IMU data at the current time horizon
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calculateOutputStates(_newest_high_rate_imu_sample);
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return updated;
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return false;
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}
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bool Ekf::initialiseFilter()
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@@ -146,7 +144,7 @@ bool Ekf::initialiseFilter()
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if (_mag_buffer) {
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magSample mag_sample;
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if (_mag_buffer->pop_first_older_than(_imu_sample_delayed.time_us, &mag_sample)) {
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if (_mag_buffer->pop_first_older_than(_time_delayed_us, &mag_sample)) {
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if (mag_sample.time_us != 0) {
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if (_mag_counter == 0) {
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_mag_lpf.reset(mag_sample.mag);
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@@ -201,12 +199,12 @@ bool Ekf::initialiseFilter()
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initHagl();
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// reset the essential fusion timeout counters
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_time_last_hgt_fuse = _imu_sample_delayed.time_us;
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_time_last_hor_pos_fuse = _imu_sample_delayed.time_us;
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_time_last_hor_vel_fuse = _imu_sample_delayed.time_us;
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_time_last_hgt_fuse = _time_delayed_us;
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_time_last_hor_pos_fuse = _time_delayed_us;
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_time_last_hor_vel_fuse = _time_delayed_us;
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// reset the output predictor state history to match the EKF initial values
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alignOutputFilter();
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_output_predictor.alignOutputFilter(_state.quat_nominal, _state.vel, _state.pos);
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return true;
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}
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@@ -233,14 +231,14 @@ bool Ekf::initialiseTilt()
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return true;
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}
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void Ekf::predictState()
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void Ekf::predictState(const imuSample &imu_delayed)
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{
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// apply imu bias corrections
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const Vector3f delta_ang_bias_scaled = getGyroBias() * _imu_sample_delayed.delta_ang_dt;
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Vector3f corrected_delta_ang = _imu_sample_delayed.delta_ang - delta_ang_bias_scaled;
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const Vector3f delta_ang_bias_scaled = getGyroBias() * imu_delayed.delta_ang_dt;
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Vector3f corrected_delta_ang = imu_delayed.delta_ang - delta_ang_bias_scaled;
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// subtract component of angular rate due to earth rotation
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corrected_delta_ang -= _R_to_earth.transpose() * _earth_rate_NED * _imu_sample_delayed.delta_ang_dt;
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corrected_delta_ang -= _R_to_earth.transpose() * _earth_rate_NED * imu_delayed.delta_ang_dt;
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const Quatf dq(AxisAnglef{corrected_delta_ang});
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@@ -249,15 +247,10 @@ void Ekf::predictState()
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_R_to_earth = Dcmf(_state.quat_nominal);
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// Calculate an earth frame delta velocity
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const Vector3f delta_vel_bias_scaled = getAccelBias() * _imu_sample_delayed.delta_vel_dt;
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const Vector3f corrected_delta_vel = _imu_sample_delayed.delta_vel - delta_vel_bias_scaled;
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const Vector3f delta_vel_bias_scaled = getAccelBias() * imu_delayed.delta_vel_dt;
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const Vector3f corrected_delta_vel = imu_delayed.delta_vel - delta_vel_bias_scaled;
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const Vector3f corrected_delta_vel_ef = _R_to_earth * corrected_delta_vel;
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// calculate a filtered horizontal acceleration with a 1 sec time constant
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// this are used for manoeuvre detection elsewhere
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const float alpha = 1.0f - _imu_sample_delayed.delta_vel_dt;
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_accel_lpf_NE = _accel_lpf_NE * alpha + corrected_delta_vel_ef.xy();
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// save the previous value of velocity so we can use trapzoidal integration
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const Vector3f vel_last = _state.vel;
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@@ -265,15 +258,15 @@ void Ekf::predictState()
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_state.vel += corrected_delta_vel_ef;
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// compensate for acceleration due to gravity
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_state.vel(2) += CONSTANTS_ONE_G * _imu_sample_delayed.delta_vel_dt;
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_state.vel(2) += CONSTANTS_ONE_G * imu_delayed.delta_vel_dt;
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// predict position states via trapezoidal integration of velocity
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_state.pos += (vel_last + _state.vel) * _imu_sample_delayed.delta_vel_dt * 0.5f;
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_state.pos += (vel_last + _state.vel) * imu_delayed.delta_vel_dt * 0.5f;
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constrainStates();
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// calculate an average filter update time
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float input = 0.5f * (_imu_sample_delayed.delta_vel_dt + _imu_sample_delayed.delta_ang_dt);
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float input = 0.5f * (imu_delayed.delta_vel_dt + imu_delayed.delta_ang_dt);
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// filter and limit input between -50% and +100% of nominal value
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const float filter_update_s = 1e-6f * _params.filter_update_interval_us;
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@@ -283,242 +276,21 @@ void Ekf::predictState()
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// some calculations elsewhere in code require a raw angular rate vector so calculate here to avoid duplication
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// protect against possible small timesteps resulting from timing slip on previous frame that can drive spikes into the rate
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// due to insufficient averaging
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if (_imu_sample_delayed.delta_ang_dt > 0.25f * _dt_ekf_avg) {
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_ang_rate_delayed_raw = _imu_sample_delayed.delta_ang / _imu_sample_delayed.delta_ang_dt;
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if (imu_delayed.delta_ang_dt > 0.25f * _dt_ekf_avg) {
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_ang_rate_delayed_raw = imu_delayed.delta_ang / imu_delayed.delta_ang_dt;
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}
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}
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/*
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* Implement a strapdown INS algorithm using the latest IMU data at the current time horizon.
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* Buffer the INS states and calculate the difference with the EKF states at the delayed fusion time horizon.
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* Calculate delta angle, delta velocity and velocity corrections from the differences and apply them at the
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* current time horizon so that the INS states track the EKF states at the delayed fusion time horizon.
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* The inspiration for using a complementary filter to correct for time delays in the EKF
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* is based on the work by A Khosravian:
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* “Recursive Attitude Estimation in the Presence of Multi-rate and Multi-delay Vector Measurements”
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* A Khosravian, J Trumpf, R Mahony, T Hamel, Australian National University
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*/
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void Ekf::calculateOutputStates(const imuSample &imu)
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{
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// Use full rate IMU data at the current time horizon
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// correct delta angles for bias offsets
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const Vector3f delta_ang_bias_scaled = getGyroBias() * imu.delta_ang_dt;
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// Apply corrections to the delta angle required to track the quaternion states at the EKF fusion time horizon
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const Vector3f delta_angle(imu.delta_ang - delta_ang_bias_scaled + _delta_angle_corr);
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// calculate a filtered horizontal acceleration with a 1 sec time constant
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// this are used for manoeuvre detection elsewhere
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const float alpha = 1.0f - imu_delayed.delta_vel_dt;
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_accel_lpf_NE = _accel_lpf_NE * alpha + corrected_delta_vel_ef.xy();
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// calculate a yaw change about the earth frame vertical
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const float spin_del_ang_D = delta_angle.dot(Vector3f(_R_to_earth_now.row(2)));
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const float spin_del_ang_D = corrected_delta_ang.dot(Vector3f(_R_to_earth.row(2)));
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_yaw_delta_ef += spin_del_ang_D;
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// Calculate filtered yaw rate to be used by the magnetometer fusion type selection logic
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// Note fixed coefficients are used to save operations. The exact time constant is not important.
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_yaw_rate_lpf_ef = 0.95f * _yaw_rate_lpf_ef + 0.05f * spin_del_ang_D / imu.delta_ang_dt;
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_output_new.time_us = imu.time_us;
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_output_vert_new.time_us = imu.time_us;
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const Quatf dq(AxisAnglef{delta_angle});
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// rotate the previous INS quaternion by the delta quaternions
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_output_new.quat_nominal = _output_new.quat_nominal * dq;
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// the quaternions must always be normalised after modification
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_output_new.quat_nominal.normalize();
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// calculate the rotation matrix from body to earth frame
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_R_to_earth_now = Dcmf(_output_new.quat_nominal);
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// correct delta velocity for bias offsets
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const Vector3f delta_vel_bias_scaled = (_state.delta_vel_bias / _dt_ekf_avg) * _imu_sample_delayed.delta_vel_dt;
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const Vector3f delta_vel_body{imu.delta_vel - delta_vel_bias_scaled};
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// rotate the delta velocity to earth frame
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Vector3f delta_vel_earth{_R_to_earth_now * delta_vel_body};
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// correct for measured acceleration due to gravity
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delta_vel_earth(2) += CONSTANTS_ONE_G * imu.delta_vel_dt;
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// calculate the earth frame velocity derivatives
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if (imu.delta_vel_dt > 1e-4f) {
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_vel_deriv = delta_vel_earth * (1.0f / imu.delta_vel_dt);
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}
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// save the previous velocity so we can use trapezoidal integration
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const Vector3f vel_last(_output_new.vel);
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// increment the INS velocity states by the measurement plus corrections
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// do the same for vertical state used by alternative correction algorithm
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_output_new.vel += delta_vel_earth;
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_output_vert_new.vert_vel += delta_vel_earth(2);
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// use trapezoidal integration to calculate the INS position states
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// do the same for vertical state used by alternative correction algorithm
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const Vector3f delta_pos_NED = (_output_new.vel + vel_last) * (imu.delta_vel_dt * 0.5f);
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_output_new.pos += delta_pos_NED;
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_output_vert_new.vert_vel_integ += delta_pos_NED(2);
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// accumulate the time for each update
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_output_vert_new.dt += imu.delta_vel_dt;
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// correct velocity for IMU offset
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if (imu.delta_ang_dt > 1e-4f) {
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// calculate the average angular rate across the last IMU update
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const Vector3f ang_rate = imu.delta_ang * (1.0f / imu.delta_ang_dt);
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// calculate the velocity of the IMU relative to the body origin
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const Vector3f vel_imu_rel_body = ang_rate % _params.imu_pos_body;
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// rotate the relative velocity into earth frame
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_vel_imu_rel_body_ned = _R_to_earth_now * vel_imu_rel_body;
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}
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// store the INS states in a ring buffer with the same length and time coordinates as the IMU data buffer
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if (_imu_updated) {
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_output_buffer.push(_output_new);
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_output_vert_buffer.push(_output_vert_new);
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// get the oldest INS state data from the ring buffer
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// this data will be at the EKF fusion time horizon
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// TODO: there is no guarantee that data is at delayed fusion horizon
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// Shouldnt we use pop_first_older_than?
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const outputSample &output_delayed = _output_buffer.get_oldest();
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const outputVert &output_vert_delayed = _output_vert_buffer.get_oldest();
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// calculate the quaternion delta between the INS and EKF quaternions at the EKF fusion time horizon
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const Quatf q_error((_state.quat_nominal.inversed() * output_delayed.quat_nominal).normalized());
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// convert the quaternion delta to a delta angle
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const float scalar = (q_error(0) >= 0.0f) ? -2.f : 2.f;
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const Vector3f delta_ang_error{scalar * q_error(1), scalar * q_error(2), scalar * q_error(3)};
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// calculate a gain that provides tight tracking of the estimator attitude states and
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// adjust for changes in time delay to maintain consistent damping ratio of ~0.7
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const float time_delay = fmaxf((imu.time_us - _imu_sample_delayed.time_us) * 1e-6f, _dt_imu_avg);
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const float att_gain = 0.5f * _dt_imu_avg / time_delay;
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// calculate a corrrection to the delta angle
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// that will cause the INS to track the EKF quaternions
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_delta_angle_corr = delta_ang_error * att_gain;
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_output_tracking_error(0) = delta_ang_error.norm();
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/*
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* Loop through the output filter state history and apply the corrections to the velocity and position states.
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* This method is too expensive to use for the attitude states due to the quaternion operations required
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* but because it eliminates the time delay in the 'correction loop' it allows higher tracking gains
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* to be used and reduces tracking error relative to EKF states.
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*/
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// Complementary filter gains
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const float vel_gain = _dt_ekf_avg / math::constrain(_params.vel_Tau, _dt_ekf_avg, 10.0f);
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const float pos_gain = _dt_ekf_avg / math::constrain(_params.pos_Tau, _dt_ekf_avg, 10.0f);
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// calculate down velocity and position tracking errors
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const float vert_vel_err = (_state.vel(2) - output_vert_delayed.vert_vel);
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const float vert_vel_integ_err = (_state.pos(2) - output_vert_delayed.vert_vel_integ);
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// calculate a velocity correction that will be applied to the output state history
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// using a PD feedback tuned to a 5% overshoot
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const float vert_vel_correction = vert_vel_integ_err * pos_gain + vert_vel_err * vel_gain * 1.1f;
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applyCorrectionToVerticalOutputBuffer(vert_vel_correction);
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// calculate velocity and position tracking errors
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const Vector3f vel_err(_state.vel - output_delayed.vel);
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const Vector3f pos_err(_state.pos - output_delayed.pos);
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_output_tracking_error(1) = vel_err.norm();
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_output_tracking_error(2) = pos_err.norm();
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// calculate a velocity correction that will be applied to the output state history
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_vel_err_integ += vel_err;
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const Vector3f vel_correction = vel_err * vel_gain + _vel_err_integ * sq(vel_gain) * 0.1f;
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// calculate a position correction that will be applied to the output state history
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_pos_err_integ += pos_err;
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const Vector3f pos_correction = pos_err * pos_gain + _pos_err_integ * sq(pos_gain) * 0.1f;
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applyCorrectionToOutputBuffer(vel_correction, pos_correction);
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}
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}
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/*
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* Calculate a correction to be applied to vert_vel that casues vert_vel_integ to track the EKF
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* down position state at the fusion time horizon using an alternative algorithm to what
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* is used for the vel and pos state tracking. The algorithm applies a correction to the vert_vel
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* state history and propagates vert_vel_integ forward in time using the corrected vert_vel history.
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* This provides an alternative vertical velocity output that is closer to the first derivative
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* of the position but does degrade tracking relative to the EKF state.
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*/
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void Ekf::applyCorrectionToVerticalOutputBuffer(float vert_vel_correction)
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{
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// loop through the vertical output filter state history starting at the oldest and apply the corrections to the
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// vert_vel states and propagate vert_vel_integ forward using the corrected vert_vel
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uint8_t index = _output_vert_buffer.get_oldest_index();
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const uint8_t size = _output_vert_buffer.get_length();
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for (uint8_t counter = 0; counter < (size - 1); counter++) {
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const uint8_t index_next = (index + 1) % size;
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outputVert ¤t_state = _output_vert_buffer[index];
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outputVert &next_state = _output_vert_buffer[index_next];
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// correct the velocity
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if (counter == 0) {
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current_state.vert_vel += vert_vel_correction;
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}
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next_state.vert_vel += vert_vel_correction;
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// position is propagated forward using the corrected velocity and a trapezoidal integrator
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next_state.vert_vel_integ = current_state.vert_vel_integ + (current_state.vert_vel + next_state.vert_vel) * 0.5f * next_state.dt;
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// advance the index
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index = (index + 1) % size;
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}
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// update output state to corrected values
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_output_vert_new = _output_vert_buffer.get_newest();
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// reset time delta to zero for the next accumulation of full rate IMU data
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_output_vert_new.dt = 0.0f;
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}
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/*
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* Calculate corrections to be applied to vel and pos output state history.
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* The vel and pos state history are corrected individually so they track the EKF states at
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* the fusion time horizon. This option provides the most accurate tracking of EKF states.
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*/
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void Ekf::applyCorrectionToOutputBuffer(const Vector3f &vel_correction, const Vector3f &pos_correction)
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{
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// loop through the output filter state history and apply the corrections to the velocity and position states
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for (uint8_t index = 0; index < _output_buffer.get_length(); index++) {
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// a constant velocity correction is applied
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_output_buffer[index].vel += vel_correction;
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// a constant position correction is applied
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_output_buffer[index].pos += pos_correction;
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}
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// update output state to corrected values
|
||||
_output_new = _output_buffer.get_newest();
|
||||
}
|
||||
|
||||
/*
|
||||
* Predict the previous quaternion output state forward using the latest IMU delta angle data.
|
||||
*/
|
||||
Quatf Ekf::calculate_quaternion() const
|
||||
{
|
||||
// Correct delta angle data for bias errors using bias state estimates from the EKF and also apply
|
||||
// corrections required to track the EKF quaternion states
|
||||
const Vector3f delta_ang_bias_scaled = getGyroBias() * _newest_high_rate_imu_sample.delta_ang_dt;
|
||||
|
||||
const Vector3f delta_angle{_newest_high_rate_imu_sample.delta_ang - delta_ang_bias_scaled + _delta_angle_corr};
|
||||
|
||||
// increment the quaternions using the corrected delta angle vector
|
||||
// the quaternions must always be normalised after modification
|
||||
return Quatf{_output_new.quat_nominal * AxisAnglef{delta_angle}}.unit();
|
||||
_yaw_rate_lpf_ef = 0.95f * _yaw_rate_lpf_ef + 0.05f * spin_del_ang_D / imu_delayed.delta_ang_dt;
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user