/**************************************************************************** * * Copyright (c) 2015 Estimation and Control Library (ECL). All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * 3. Neither the name ECL nor the names of its contributors may be * used to endorse or promote products derived from this software * without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * ****************************************************************************/ /** * @file ekf.cpp * Core functions for ekf attitude and position estimator. * * @author Roman Bast * @author Paul Riseborough */ #include "ekf.h" #include bool Ekf::init(uint64_t timestamp) { if (!_initialised) { _initialised = initialise_interface(timestamp); reset(); } return _initialised; } void Ekf::reset() { ECL_INFO("reset"); _state.vel.setZero(); _state.pos.setZero(); _state.gyro_bias.setZero(); _state.accel_bias.setZero(); _state.mag_I.setZero(); _state.mag_B.setZero(); _state.wind_vel.setZero(); _state.quat_nominal.setIdentity(); #if defined(CONFIG_EKF2_RANGE_FINDER) _range_sensor.setPitchOffset(_params.rng_sens_pitch); _range_sensor.setCosMaxTilt(_params.range_cos_max_tilt); _range_sensor.setQualityHysteresis(_params.range_valid_quality_s); #endif // CONFIG_EKF2_RANGE_FINDER _control_status.value = 0; _control_status_prev.value = 0; _control_status.flags.in_air = true; _control_status_prev.flags.in_air = true; _ang_rate_delayed_raw.zero(); _fault_status.value = 0; _innov_check_fail_status.value = 0; _prev_gyro_bias_var.zero(); _prev_accel_bias_var.zero(); resetGpsDriftCheckFilters(); _output_predictor.reset(); // Ekf private fields _time_last_horizontal_aiding = 0; _time_last_v_pos_aiding = 0; _time_last_v_vel_aiding = 0; _time_last_hor_pos_fuse = 0; _time_last_hgt_fuse = 0; _time_last_hor_vel_fuse = 0; _time_last_ver_vel_fuse = 0; _time_last_heading_fuse = 0; _time_last_zero_velocity_fuse = 0; _last_known_pos.setZero(); _time_acc_bias_check = 0; _gps_checks_passed = false; _gps_alt_ref = NAN; _baro_counter = 0; #if defined(CONFIG_EKF2_MAGNETOMETER) _mag_counter = 0; #endif // CONFIG_EKF2_MAGNETOMETER _time_bad_vert_accel = 0; _time_good_vert_accel = 0; _clip_counter = 0; resetEstimatorAidStatus(_aid_src_baro_hgt); #if defined(CONFIG_EKF2_AIRSPEED) resetEstimatorAidStatus(_aid_src_airspeed); #endif // CONFIG_EKF2_AIRSPEED #if defined(CONFIG_EKF2_SIDESLIP) resetEstimatorAidStatus(_aid_src_sideslip); #endif // CONFIG_EKF2_SIDESLIP resetEstimatorAidStatus(_aid_src_fake_pos); resetEstimatorAidStatus(_aid_src_fake_hgt); #if defined(CONFIG_EKF2_EXTERNAL_VISION) resetEstimatorAidStatus(_aid_src_ev_hgt); resetEstimatorAidStatus(_aid_src_ev_pos); resetEstimatorAidStatus(_aid_src_ev_vel); resetEstimatorAidStatus(_aid_src_ev_yaw); #endif // CONFIG_EKF2_EXTERNAL_VISION resetEstimatorAidStatus(_aid_src_gnss_hgt); resetEstimatorAidStatus(_aid_src_gnss_pos); resetEstimatorAidStatus(_aid_src_gnss_vel); #if defined(CONFIG_EKF2_GNSS_YAW) resetEstimatorAidStatus(_aid_src_gnss_yaw); #endif // CONFIG_EKF2_GNSS_YAW #if defined(CONFIG_EKF2_MAGNETOMETER) resetEstimatorAidStatus(_aid_src_mag_heading); resetEstimatorAidStatus(_aid_src_mag); #endif // CONFIG_EKF2_MAGNETOMETER #if defined(CONFIG_EKF2_AUXVEL) resetEstimatorAidStatus(_aid_src_aux_vel); #endif // CONFIG_EKF2_AUXVEL #if defined(CONFIG_EKF2_OPTICAL_FLOW) resetEstimatorAidStatus(_aid_src_optical_flow); resetEstimatorAidStatus(_aid_src_terrain_optical_flow); #endif // CONFIG_EKF2_OPTICAL_FLOW #if defined(CONFIG_EKF2_RANGE_FINDER) resetEstimatorAidStatus(_aid_src_rng_hgt); #endif // CONFIG_EKF2_RANGE_FINDER } bool Ekf::update() { if (!_filter_initialised) { _filter_initialised = initialiseFilter(); if (!_filter_initialised) { return false; } } // Only run the filter if IMU data in the buffer has been updated if (_imu_updated) { _imu_updated = false; // get the oldest IMU data from the buffer // TODO: explicitly pop at desired time horizon const imuSample imu_sample_delayed = _imu_buffer.get_oldest(); // perform state and covariance prediction for the main filter predictCovariance(imu_sample_delayed); predictState(imu_sample_delayed); // control fusion of observation data controlFusionModes(imu_sample_delayed); #if defined(CONFIG_EKF2_TERRAIN) // run a separate filter for terrain estimation runTerrainEstimator(imu_sample_delayed); #endif // CONFIG_EKF2_TERRAIN _output_predictor.correctOutputStates(imu_sample_delayed.time_us, _state.quat_nominal, _state.vel, _state.pos, _state.gyro_bias, _state.accel_bias); return true; } return false; } bool Ekf::initialiseFilter() { // Filter accel for tilt initialization const imuSample &imu_init = _imu_buffer.get_newest(); // protect against zero data if (imu_init.delta_vel_dt < 1e-4f || imu_init.delta_ang_dt < 1e-4f) { return false; } if (_is_first_imu_sample) { _accel_lpf.reset(imu_init.delta_vel / imu_init.delta_vel_dt); _gyro_lpf.reset(imu_init.delta_ang / imu_init.delta_ang_dt); _is_first_imu_sample = false; } else { _accel_lpf.update(imu_init.delta_vel / imu_init.delta_vel_dt); _gyro_lpf.update(imu_init.delta_ang / imu_init.delta_ang_dt); } if (!initialiseTilt()) { return false; } // initialise the state covariance matrix now we have starting values for all the states initialiseCovariance(); #if defined(CONFIG_EKF2_TERRAIN) // Initialise the terrain estimator initHagl(); #endif // CONFIG_EKF2_TERRAIN // reset the output predictor state history to match the EKF initial values _output_predictor.alignOutputFilter(_state.quat_nominal, _state.vel, _state.pos); return true; } bool Ekf::initialiseTilt() { const float accel_norm = _accel_lpf.getState().norm(); const float gyro_norm = _gyro_lpf.getState().norm(); if (accel_norm < 0.8f * CONSTANTS_ONE_G || accel_norm > 1.2f * CONSTANTS_ONE_G || gyro_norm > math::radians(15.0f)) { return false; } // get initial tilt estimate from delta velocity vector, assuming vehicle is static _state.quat_nominal = Quatf(_accel_lpf.getState(), Vector3f(0.f, 0.f, -1.f)); _R_to_earth = Dcmf(_state.quat_nominal); return true; } void Ekf::predictState(const imuSample &imu_delayed) { // apply imu bias corrections const Vector3f delta_ang_bias_scaled = getGyroBias() * imu_delayed.delta_ang_dt; Vector3f corrected_delta_ang = imu_delayed.delta_ang - delta_ang_bias_scaled; // subtract component of angular rate due to earth rotation corrected_delta_ang -= _R_to_earth.transpose() * _earth_rate_NED * imu_delayed.delta_ang_dt; const Quatf dq(AxisAnglef{corrected_delta_ang}); // rotate the previous quaternion by the delta quaternion using a quaternion multiplication _state.quat_nominal = (_state.quat_nominal * dq).normalized(); _R_to_earth = Dcmf(_state.quat_nominal); // Calculate an earth frame delta velocity const Vector3f delta_vel_bias_scaled = getAccelBias() * imu_delayed.delta_vel_dt; const Vector3f corrected_delta_vel = imu_delayed.delta_vel - delta_vel_bias_scaled; const Vector3f corrected_delta_vel_ef = _R_to_earth * corrected_delta_vel; // save the previous value of velocity so we can use trapzoidal integration const Vector3f vel_last = _state.vel; // calculate the increment in velocity using the current orientation _state.vel += corrected_delta_vel_ef; // compensate for acceleration due to gravity _state.vel(2) += CONSTANTS_ONE_G * imu_delayed.delta_vel_dt; // predict position states via trapezoidal integration of velocity _state.pos += (vel_last + _state.vel) * imu_delayed.delta_vel_dt * 0.5f; constrainStates(); // calculate an average filter update time float input = 0.5f * (imu_delayed.delta_vel_dt + imu_delayed.delta_ang_dt); // filter and limit input between -50% and +100% of nominal value const float filter_update_s = 1e-6f * _params.filter_update_interval_us; input = math::constrain(input, 0.5f * filter_update_s, 2.f * filter_update_s); _dt_ekf_avg = 0.99f * _dt_ekf_avg + 0.01f * input; // some calculations elsewhere in code require a raw angular rate vector so calculate here to avoid duplication // protect against possible small timesteps resulting from timing slip on previous frame that can drive spikes into the rate // due to insufficient averaging if (imu_delayed.delta_ang_dt > 0.25f * _dt_ekf_avg) { _ang_rate_delayed_raw = imu_delayed.delta_ang / imu_delayed.delta_ang_dt; } // calculate a filtered horizontal acceleration with a 1 sec time constant // this are used for manoeuvre detection elsewhere const float alpha = 1.0f - imu_delayed.delta_vel_dt; _accel_lpf_NE = _accel_lpf_NE * alpha + corrected_delta_vel_ef.xy(); // calculate a yaw change about the earth frame vertical const float spin_del_ang_D = corrected_delta_ang.dot(Vector3f(_R_to_earth.row(2))); _yaw_delta_ef += spin_del_ang_D; // Calculate filtered yaw rate to be used by the magnetometer fusion type selection logic // Note fixed coefficients are used to save operations. The exact time constant is not important. _yaw_rate_lpf_ef = 0.95f * _yaw_rate_lpf_ef + 0.05f * spin_del_ang_D / imu_delayed.delta_ang_dt; }