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bresch
116 lines
5.6 KiB
C++
116 lines
5.6 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2023 PX4 Development Team. 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 PX4 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 gravity_fusion.cpp
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* Fuse observations from the gravity vector to constrain roll
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* and pitch (a la complementary filter).
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*
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* @author Daniel M. Sahu <danielmohansahu@gmail.com>
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*/
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#include "ekf.h"
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#include <ekf_derivation/generated/compute_gravity_xyz_innov_var_and_hx.h>
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#include <ekf_derivation/generated/compute_gravity_y_innov_var_and_h.h>
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#include <ekf_derivation/generated/compute_gravity_z_innov_var_and_h.h>
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#include <mathlib/mathlib.h>
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void Ekf::controlGravityFusion(const imuSample &imu)
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{
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// get raw accelerometer reading at delayed horizon and expected measurement noise (gaussian)
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const Vector3f measurement = Vector3f(imu.delta_vel / imu.delta_vel_dt - _state.accel_bias).unit();
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const float measurement_var = math::max(sq(_params.gravity_noise), sq(0.01f));
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const float upper_accel_limit = CONSTANTS_ONE_G * 1.1f;
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const float lower_accel_limit = CONSTANTS_ONE_G * 0.9f;
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const bool accel_lpf_norm_good = (_accel_magnitude_filt > lower_accel_limit)
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&& (_accel_magnitude_filt < upper_accel_limit);
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// fuse gravity observation if our overall acceleration isn't too big
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_control_status.flags.gravity_vector = (_params.imu_ctrl & static_cast<int32_t>(ImuCtrl::GravityVector))
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&& (accel_lpf_norm_good || _control_status.flags.vehicle_at_rest)
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&& !isHorizontalAidingActive();
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// calculate kalman gains and innovation variances
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Vector3f innovation = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f)) - measurement;
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Vector3f innovation_variance;
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const auto state_vector = _state.vector();
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VectorState H;
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sym::ComputeGravityXyzInnovVarAndHx(state_vector, P, measurement_var, &innovation_variance, &H);
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// fill estimator aid source status
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updateAidSourceStatus(_aid_src_gravity,
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imu.time_us, // sample timestamp
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measurement, // observation
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Vector3f{measurement_var, measurement_var, measurement_var}, // observation variance
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innovation, // innovation
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innovation_variance, // innovation variance
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0.25f); // innovation gate
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// update the states and covariance using sequential fusion
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for (uint8_t index = 0; index <= 2; index++) {
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// Calculate Kalman gains and observation jacobians
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if (index == 0) {
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// everything was already computed above
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} else if (index == 1) {
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// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
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sym::ComputeGravityYInnovVarAndH(state_vector, P, measurement_var, &_aid_src_gravity.innovation_variance[index], &H);
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// recalculate innovation using the updated state
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_aid_src_gravity.innovation[index] = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f,
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-1.f))(index) - measurement(index);
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} else if (index == 2) {
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// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
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sym::ComputeGravityZInnovVarAndH(state_vector, P, measurement_var, &_aid_src_gravity.innovation_variance[index], &H);
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// recalculate innovation using the updated state
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_aid_src_gravity.innovation[index] = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f,
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-1.f))(index) - measurement(index);
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}
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VectorState K = P * H / _aid_src_gravity.innovation_variance[index];
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const bool accel_clipping = imu.delta_vel_clipping[0] || imu.delta_vel_clipping[1] || imu.delta_vel_clipping[2];
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if (_control_status.flags.gravity_vector && !_aid_src_gravity.innovation_rejected && !accel_clipping) {
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measurementUpdate(K, H, _aid_src_gravity.observation_variance[index], _aid_src_gravity.innovation[index]);
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}
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}
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_aid_src_gravity.fused = true;
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_aid_src_gravity.time_last_fuse = imu.time_us;
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}
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