forked from Archive/PX4-Autopilot
672 lines
25 KiB
C++
672 lines
25 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2015 Estimation and Control Library (ECL). All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name ECL nor the names of its contributors may be
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* used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
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* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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****************************************************************************/
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/**
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* @file ekf.cpp
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* Core functions for ekf attitude and position estimator.
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*
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* @author Roman Bast <bapstroman@gmail.com>
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* @author Paul Riseborough <p_riseborough@live.com.au>
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*/
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#include "ekf.h"
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#include <ecl.h>
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#include <mathlib/mathlib.h>
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bool Ekf::init(uint64_t timestamp)
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{
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bool ret = initialise_interface(timestamp);
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_state.vel.setZero();
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_state.pos.setZero();
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_state.gyro_bias.setZero();
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_state.accel_bias.setZero();
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_state.mag_I.setZero();
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_state.mag_B.setZero();
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_state.wind_vel.setZero();
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_state.quat_nominal.setZero();
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_state.quat_nominal(0) = 1.0f;
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_output_new.vel.setZero();
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_output_new.pos.setZero();
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_output_new.quat_nominal.setZero();
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_delta_angle_corr.setZero();
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_imu_down_sampled.delta_ang.setZero();
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_imu_down_sampled.delta_vel.setZero();
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_imu_down_sampled.delta_ang_dt = 0.0f;
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_imu_down_sampled.delta_vel_dt = 0.0f;
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_imu_down_sampled.time_us = timestamp;
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_q_down_sampled(0) = 1.0f;
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_q_down_sampled(1) = 0.0f;
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_q_down_sampled(2) = 0.0f;
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_q_down_sampled(3) = 0.0f;
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_imu_updated = false;
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_NED_origin_initialised = false;
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_gps_speed_valid = false;
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_filter_initialised = false;
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_terrain_initialised = false;
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_sin_tilt_rng = sinf(_params.rng_sens_pitch);
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_cos_tilt_rng = cosf(_params.rng_sens_pitch);
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_control_status.value = 0;
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_control_status_prev.value = 0;
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_dt_ekf_avg = FILTER_UPDATE_PERIOD_S;
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_fault_status.value = 0;
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_innov_check_fail_status.value = 0;
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_accel_mag_filt = 0.0f;
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_ang_rate_mag_filt = 0.0f;
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_prev_dvel_bias_var.zero();
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return ret;
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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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if (!_filter_initialised) {
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return false;
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}
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}
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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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// perform state and covariance prediction for the main filter
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predictState();
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predictCovariance();
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// control fusion of observation data
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controlFusionModes();
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// run a separate filter for terrain estimation
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runTerrainEstimator();
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updated = 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();
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return updated;
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}
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bool Ekf::initialiseFilter()
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{
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// Keep accumulating measurements until we have a minimum of 10 samples for the required sensors
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// Sum the IMU delta angle measurements
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const imuSample &imu_init = _imu_buffer.get_newest();
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_delVel_sum += imu_init.delta_vel;
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// Sum the magnetometer measurements
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if (_mag_buffer.pop_first_older_than(_imu_sample_delayed.time_us, &_mag_sample_delayed)) {
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if ((_mag_counter == 0) && (_mag_sample_delayed.time_us != 0)) {
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// initialise the counter when we start getting data from the buffer
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_mag_counter = 1;
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} else if ((_mag_counter != 0) && (_mag_sample_delayed.time_us != 0)) {
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// increment the sample count and apply a LPF to the measurement
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_mag_counter ++;
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// don't start using data until we can be certain all bad initial data has been flushed
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if (_mag_counter == (uint8_t)(_obs_buffer_length + 1)) {
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// initialise filter states
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_mag_filt_state = _mag_sample_delayed.mag;
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} else if (_mag_counter > (uint8_t)(_obs_buffer_length + 1)) {
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// noise filter the data
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_mag_filt_state = _mag_filt_state * 0.9f + _mag_sample_delayed.mag * 0.1f;
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}
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}
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}
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// Count the number of external vision measurements received
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if (_ext_vision_buffer.pop_first_older_than(_imu_sample_delayed.time_us, &_ev_sample_delayed)) {
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if ((_ev_counter == 0) && (_ev_sample_delayed.time_us != 0)) {
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// initialise the counter
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_ev_counter = 1;
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// set the height fusion mode to use external vision data when we start getting valid data from the buffer
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if (_primary_hgt_source == VDIST_SENSOR_EV) {
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_control_status.flags.baro_hgt = false;
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_control_status.flags.gps_hgt = false;
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_control_status.flags.rng_hgt = false;
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_control_status.flags.ev_hgt = true;
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}
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} else if ((_ev_counter != 0) && (_ev_sample_delayed.time_us != 0)) {
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// increment the sample count
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_ev_counter ++;
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}
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}
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// set the default height source from the adjustable parameter
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if (_hgt_counter == 0) {
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_primary_hgt_source = _params.vdist_sensor_type;
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}
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// accumulate enough height measurements to be confident in the quality of the data
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// we use baro height initially and switch to GPS/range/EV finder later when it passes checks.
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if (_baro_buffer.pop_first_older_than(_imu_sample_delayed.time_us, &_baro_sample_delayed)) {
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if ((_hgt_counter == 0) && (_baro_sample_delayed.time_us != 0)) {
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// initialise the counter and height fusion method when we start getting data from the buffer
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setControlBaroHeight();
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_hgt_counter = 1;
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} else if ((_hgt_counter != 0) && (_baro_sample_delayed.time_us != 0)) {
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// increment the sample count and apply a LPF to the measurement
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_hgt_counter ++;
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// don't start using data until we can be certain all bad initial data has been flushed
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if (_hgt_counter == (uint8_t)(_obs_buffer_length + 1)) {
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// initialise filter states
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_baro_hgt_offset = _baro_sample_delayed.hgt;
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} else if (_hgt_counter > (uint8_t)(_obs_buffer_length + 1)) {
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// noise filter the data
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_baro_hgt_offset = 0.9f * _baro_hgt_offset + 0.1f * _baro_sample_delayed.hgt;
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}
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}
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}
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// check to see if we have enough measurements and return false if not
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bool hgt_count_fail = _hgt_counter <= 2u * _obs_buffer_length;
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bool mag_count_fail = _mag_counter <= 2u * _obs_buffer_length;
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if (hgt_count_fail || mag_count_fail) {
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return false;
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} else {
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// reset variables that are shared with post alignment GPS checks
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_gps_drift_velD = 0.0f;
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_gps_alt_ref = 0.0f;
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// Zero all of the states
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_state.vel.setZero();
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_state.pos.setZero();
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_state.gyro_bias.setZero();
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_state.accel_bias.setZero();
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_state.mag_I.setZero();
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_state.mag_B.setZero();
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_state.wind_vel.setZero();
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// get initial roll and pitch estimate from delta velocity vector, assuming vehicle is static
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float pitch = 0.0f;
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float roll = 0.0f;
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if (_delVel_sum.norm() > 0.001f) {
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_delVel_sum.normalize();
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pitch = asinf(_delVel_sum(0));
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roll = atan2f(-_delVel_sum(1), -_delVel_sum(2));
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} else {
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return false;
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}
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// calculate initial tilt alignment
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Eulerf euler_init(roll, pitch, 0.0f);
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_state.quat_nominal = Quatf(euler_init);
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_output_new.quat_nominal = _state.quat_nominal;
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// update transformation matrix from body to world frame
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_R_to_earth = quat_to_invrotmat(_state.quat_nominal);
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// calculate the averaged magnetometer reading
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Vector3f mag_init = _mag_filt_state;
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// calculate the initial magnetic field and yaw alignment
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// Get the magnetic declination
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calcMagDeclination();
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_control_status.flags.yaw_align = resetMagHeading(mag_init);
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if (_control_status.flags.rng_hgt) {
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// if we are using the range finder as the primary source, then calculate the baro height at origin so we can use baro as a backup
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// so it can be used as a backup ad set the initial height using the range finder
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const baroSample &baro_newest = _baro_buffer.get_newest();
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_baro_hgt_offset = baro_newest.hgt;
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_state.pos(2) = -math::max(_rng_filt_state * _R_rng_to_earth_2_2, _params.rng_gnd_clearance);
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ECL_INFO("EKF using range finder height - commencing alignment");
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} else if (_control_status.flags.ev_hgt) {
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// if we are using external vision data for height, then the vertical position state needs to be reset
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// because the initialisation position is not the zero datum
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resetHeight();
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}
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// initialise the state covariance matrix
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initialiseCovariance();
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// try to initialise the terrain estimator
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_terrain_initialised = initHagl();
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// reset the essential fusion timeout counters
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_time_last_hgt_fuse = _time_last_imu;
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_time_last_pos_fuse = _time_last_imu;
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_time_last_delpos_fuse = _time_last_imu;
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_time_last_vel_fuse = _time_last_imu;
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_time_last_hagl_fuse = _time_last_imu;
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_time_last_of_fuse = _time_last_imu;
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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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return true;
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}
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}
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void Ekf::predictState()
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{
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if (!_earth_rate_initialised) {
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if (_NED_origin_initialised) {
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calcEarthRateNED(_earth_rate_NED, (float)_pos_ref.lat_rad);
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_earth_rate_initialised = true;
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}
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}
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// apply imu bias corrections
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Vector3f corrected_delta_ang = _imu_sample_delayed.delta_ang - _state.gyro_bias;
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Vector3f corrected_delta_vel = _imu_sample_delayed.delta_vel - _state.accel_bias;
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// correct delta angles for earth rotation rate
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corrected_delta_ang -= -_R_to_earth.transpose() * _earth_rate_NED * _imu_sample_delayed.delta_ang_dt;
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// convert the delta angle to a delta quaternion
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Quatf dq;
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dq.from_axis_angle(corrected_delta_ang);
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// rotate the previous quaternion by the delta quaternion using a quaternion multiplication
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_state.quat_nominal = _state.quat_nominal * dq;
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// quaternions must be normalised whenever they are modified
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_state.quat_nominal.normalize();
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// save the previous value of velocity so we can use trapzoidal integration
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Vector3f vel_last = _state.vel;
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// update transformation matrix from body to world frame
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_R_to_earth = quat_to_invrotmat(_state.quat_nominal);
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// Calculate an earth frame delta velocity
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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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float alpha = 1.0f - _imu_sample_delayed.delta_vel_dt;
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_accel_lpf_NE(0) = _accel_lpf_NE(0) * alpha + corrected_delta_vel_ef(0);
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_accel_lpf_NE(1) = _accel_lpf_NE(1) * alpha + corrected_delta_vel_ef(1);
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// calculate the increment in velocity using the current orientation
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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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// 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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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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// filter and limit input between -50% and +100% of nominal value
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input = math::constrain(input, 0.5f * FILTER_UPDATE_PERIOD_S, 2.0f * FILTER_UPDATE_PERIOD_S);
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_dt_ekf_avg = 0.99f * _dt_ekf_avg + 0.01f * input;
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}
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bool Ekf::collect_imu(const imuSample &imu)
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{
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// accumulate and downsample IMU data across a period FILTER_UPDATE_PERIOD_MS long
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// copy imu data to local variables
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_imu_sample_new = imu;
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// accumulate the time deltas
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_imu_down_sampled.delta_ang_dt += imu.delta_ang_dt;
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_imu_down_sampled.delta_vel_dt += imu.delta_vel_dt;
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// use a quaternion to accumulate delta angle data
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// this quaternion represents the rotation from the start to end of the accumulation period
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Quatf delta_q;
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delta_q.rotate(imu.delta_ang);
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_q_down_sampled = _q_down_sampled * delta_q;
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_q_down_sampled.normalize();
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// rotate the accumulated delta velocity data forward each time so it is always in the updated rotation frame
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Dcmf delta_R(delta_q.inversed());
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_imu_down_sampled.delta_vel = delta_R * _imu_down_sampled.delta_vel;
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// accumulate the most recent delta velocity data at the updated rotation frame
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// assume effective sample time is halfway between the previous and current rotation frame
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_imu_down_sampled.delta_vel += (imu.delta_vel + delta_R * imu.delta_vel) * 0.5f;
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// if the target time delta between filter prediction steps has been exceeded
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// write the accumulated IMU data to the ring buffer
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const float target_dt = FILTER_UPDATE_PERIOD_S;
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if (_imu_down_sampled.delta_ang_dt >= target_dt - _imu_collection_time_adj) {
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// accumulate the amount of time to advance the IMU collection time so that we meet the
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// average EKF update rate requirement
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_imu_collection_time_adj += 0.01f * (_imu_down_sampled.delta_ang_dt - target_dt);
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_imu_collection_time_adj = math::constrain(_imu_collection_time_adj, -0.5f * target_dt, 0.5f * target_dt);
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imuSample imu_sample_new;
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imu_sample_new.delta_ang = _q_down_sampled.to_axis_angle();
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imu_sample_new.delta_vel = _imu_down_sampled.delta_vel;
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imu_sample_new.delta_ang_dt = _imu_down_sampled.delta_ang_dt;
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imu_sample_new.delta_vel_dt = _imu_down_sampled.delta_vel_dt;
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imu_sample_new.time_us = imu.time_us;
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_imu_buffer.push(imu_sample_new);
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// get the oldest data from the buffer
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_imu_sample_delayed = _imu_buffer.get_oldest();
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// calculate the minimum interval between observations required to guarantee no loss of data
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// this will occur if data is overwritten before its time stamp falls behind the fusion time horizon
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_min_obs_interval_us = (imu_sample_new.time_us - _imu_sample_delayed.time_us) / (_obs_buffer_length - 1);
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// reset
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_imu_down_sampled.delta_ang.setZero();
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_imu_down_sampled.delta_vel.setZero();
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_imu_down_sampled.delta_ang_dt = 0.0f;
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_imu_down_sampled.delta_vel_dt = 0.0f;
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_q_down_sampled(0) = 1.0f;
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_q_down_sampled(1) = _q_down_sampled(2) = _q_down_sampled(3) = 0.0f;
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_imu_updated = true;
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} else {
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_imu_updated = false;
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}
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return _imu_updated;
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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()
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{
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// Use full rate IMU data at the current time horizon
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const imuSample &imu = _imu_sample_new;
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// correct delta angles for bias offsets
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const float dt_scale_correction = _dt_imu_avg / _dt_ekf_avg;
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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 - _state.gyro_bias * dt_scale_correction + _delta_angle_corr};
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// calculate a yaw change about the earth frame vertical
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const float spin_del_ang_D = _R_to_earth_now(2, 0) * delta_angle(0) +
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_R_to_earth_now(2, 1) * delta_angle(1) +
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_R_to_earth_now(2, 2) * delta_angle(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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// convert the delta angle to an equivalent delta quaternions
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Quatf dq;
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dq.from_axis_angle(delta_angle);
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// rotate the previous INS quaternion by the delta quaternions
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_output_new.time_us = imu.time_us;
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_output_new.quat_nominal = _output_new.quat_nominal * dq;
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// the quaternions must always be normalised afer 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 = quat_to_invrotmat(_output_new.quat_nominal);
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// correct delta velocity for bias offsets
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const Vector3f delta_vel{imu.delta_vel - _state.accel_bias * dt_scale_correction};
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// rotate the delta velocity to earth frame
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Vector3f delta_vel_NED{_R_to_earth_now * delta_vel};
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// correct for measured acceleration due to gravity
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delta_vel_NED(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_ned = delta_vel_NED * (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_NED;
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_output_vert_new.vel_d += delta_vel_NED(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.vel_d_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 = cross_product(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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_output_sample_delayed = _output_buffer.get_oldest();
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_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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Quatf quat_inv = _state.quat_nominal.inversed();
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Quatf q_error = quat_inv * _output_sample_delayed.quat_nominal;
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q_error.normalize();
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// convert the quaternion delta to a delta angle
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float scalar;
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if (q_error(0) >= 0.0f) {
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scalar = -2.0f;
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} else {
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scalar = 2.0f;
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}
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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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|
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// calculate velocity and position tracking errors
|
|
const Vector3f vel_err{_state.vel - _output_sample_delayed.vel};
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|
const Vector3f pos_err{_state.pos - _output_sample_delayed.pos};
|
|
|
|
// collect magnitude tracking error for diagnostics
|
|
_output_tracking_error[0] = delta_ang_error.norm();
|
|
_output_tracking_error[1] = vel_err.norm();
|
|
_output_tracking_error[2] = pos_err.norm();
|
|
|
|
/*
|
|
* Loop through the output filter state history and apply the corrections to the velocity and position states.
|
|
* This method is too expensive to use for the attitude states due to the quaternion operations required
|
|
* but because it eliminates the time delay in the 'correction loop' it allows higher tracking gains
|
|
* to be used and reduces tracking error relative to EKF states.
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|
*/
|
|
|
|
// Complementary filter gains
|
|
const float vel_gain = _dt_ekf_avg / math::constrain(_params.vel_Tau, _dt_ekf_avg, 10.0f);
|
|
const float pos_gain = _dt_ekf_avg / math::constrain(_params.pos_Tau, _dt_ekf_avg, 10.0f);
|
|
{
|
|
/*
|
|
* Calculate a correction to be applied to vel_d that casues vel_d_integ to track the EKF
|
|
* down position state at the fusion time horizon using an alternative algorithm to what
|
|
* is used for the vel and pos state tracking. The algorithm applies a correction to the vel_d
|
|
* state history and propagates vel_d_integ forward in time using the corrected vel_d history.
|
|
* This provides an alternative vertical velocity output that is closer to the first derivative
|
|
* of the position but does degrade tracking relative to the EKF state.
|
|
*/
|
|
|
|
// calculate down velocity and position tracking errors
|
|
const float vel_d_err = (_state.vel(2) - _output_vert_delayed.vel_d);
|
|
const float pos_d_err = (_state.pos(2) - _output_vert_delayed.vel_d_integ);
|
|
|
|
// calculate a velocity correction that will be applied to the output state history
|
|
// using a PD feedback tuned to a 5% overshoot
|
|
const float vel_d_correction = pos_d_err * pos_gain + vel_d_err * pos_gain * 1.1f;
|
|
|
|
/*
|
|
* Calculate corrections to be applied to vel and pos output state history.
|
|
* The vel and pos state history are corrected individually so they track the EKF states at
|
|
* the fusion time horizon. This option provides the most accurate tracking of EKF states.
|
|
*/
|
|
|
|
// loop through the vertical output filter state history starting at the oldest and apply the corrections to the
|
|
// vel_d states and propagate vel_d_integ forward using the corrected vel_d
|
|
uint8_t index = _output_vert_buffer.get_oldest_index();
|
|
|
|
const uint8_t size = _output_vert_buffer.get_length();
|
|
|
|
for (uint8_t counter = 0; counter < (size - 1); counter++) {
|
|
const uint8_t index_next = (index + 1) % size;
|
|
outputVert ¤t_state = _output_vert_buffer[index];
|
|
outputVert &next_state = _output_vert_buffer[index_next];
|
|
|
|
// correct the velocity
|
|
if (counter == 0) {
|
|
current_state.vel_d += vel_d_correction;
|
|
}
|
|
|
|
next_state.vel_d += vel_d_correction;
|
|
|
|
// position is propagated forward using the corrected velocity and a trapezoidal integrator
|
|
next_state.vel_d_integ = current_state.vel_d_integ + (current_state.vel_d + next_state.vel_d) * 0.5f * next_state.dt;
|
|
|
|
// advance the index
|
|
index = (index + 1) % size;
|
|
}
|
|
|
|
// update output state to corrected values
|
|
_output_vert_new = _output_vert_buffer.get_newest();
|
|
|
|
// reset time delta to zero for the next accumulation of full rate IMU data
|
|
_output_vert_new.dt = 0.0f;
|
|
}
|
|
|
|
{
|
|
/*
|
|
* Calculate corrections to be applied to vel and pos output state history.
|
|
* The vel and pos state history are corrected individually so they track the EKF states at
|
|
* the fusion time horizon. This option provides the most accurate tracking of EKF states.
|
|
*/
|
|
|
|
// calculate a velocity correction that will be applied to the output state history
|
|
_vel_err_integ += vel_err;
|
|
const Vector3f vel_correction = vel_err * vel_gain + _vel_err_integ * sq(vel_gain) * 0.1f;
|
|
|
|
// calculate a position correction that will be applied to the output state history
|
|
_pos_err_integ += pos_err;
|
|
const Vector3f pos_correction = pos_err * pos_gain + _pos_err_integ * sq(pos_gain) * 0.1f;
|
|
|
|
// loop through the output filter state history and apply the corrections to the velocity and position states
|
|
for (uint8_t index = 0; index < _output_buffer.get_length(); index++) {
|
|
// a constant velocity correction is applied
|
|
_output_buffer[index].vel += vel_correction;
|
|
|
|
// a constant position correction is applied
|
|
_output_buffer[index].pos += pos_correction;
|
|
}
|
|
|
|
// 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_angle{_imu_sample_new.delta_ang - _state.gyro_bias * (_dt_imu_avg / _dt_ekf_avg) + _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();
|
|
}
|