forked from Archive/PX4-Autopilot
310 lines
12 KiB
C++
310 lines
12 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 vel_pos_fusion.cpp
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* Function for fusing gps and baro measurements/
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*
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* @author Roman Bast <bapstroman@gmail.com>
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* @author Siddharth Bharat Purohit <siddharthbharatpurohit@gmail.com>
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* @author Paul Riseborough <p_riseborough@live.com.au>
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*
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*/
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#include "ekf.h"
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#include "mathlib.h"
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void Ekf::fuseVelPosHeight()
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{
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bool fuse_map[6] = {}; // map of booleans true when [VN,VE,VD,PN,PE,PD] observations are available
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bool innov_check_pass_map[6] = {}; // true when innovations consistency checks pass for [VN,VE,VD,PN,PE,PD] observations
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float R[6] = {}; // observation variances for [VN,VE,VD,PN,PE,PD]
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float gate_size[6] = {}; // innovation consistency check gate sizes for [VN,VE,VD,PN,PE,PD] observations
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float Kfusion[24] = {}; // Kalman gain vector for any single observation - sequential fusion is used
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// calculate innovations, innovations gate sizes and observation variances
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if (_fuse_hor_vel) {
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fuse_map[0] = fuse_map[1] = true;
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// horizontal velocity innovations
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_vel_pos_innov[0] = _state.vel(0) - _gps_sample_delayed.vel(0);
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_vel_pos_innov[1] = _state.vel(1) - _gps_sample_delayed.vel(1);
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// observation variance - use receiver reported accuracy with parameter setting the minimum value
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R[0] = fmaxf(_params.gps_vel_noise, 0.01f);
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R[0] = fmaxf(R[0], _gps_sample_delayed.sacc);
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R[0] = R[0] * R[0];
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R[1] = R[0];
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// innovation gate sizes
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gate_size[0] = fmaxf(_params.vel_innov_gate, 1.0f);
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gate_size[1] = gate_size[0];
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}
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if (_fuse_vert_vel) {
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fuse_map[2] = true;
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// vertical velocity innovation
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_vel_pos_innov[2] = _state.vel(2) - _gps_sample_delayed.vel(2);
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// observation variance - use receiver reported accuracy with parameter setting the minimum value
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R[2] = fmaxf(_params.gps_vel_noise, 0.01f);
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// use scaled horizontal speed accuracy assuming typical ratio of VDOP/HDOP
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R[2] = 1.5f * fmaxf(R[2], _gps_sample_delayed.sacc);
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R[2] = R[2] * R[2];
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// innovation gate size
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gate_size[2] = fmaxf(_params.vel_innov_gate, 1.0f);
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}
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if (_fuse_pos) {
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fuse_map[3] = fuse_map[4] = true;
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// Calculate innovations and observation variance depending on type of observations
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// being used
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if (_control_status.flags.gps) {
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// we are using GPS measurements
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float lower_limit = fmaxf(_params.gps_pos_noise, 0.01f);
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float upper_limit = fmaxf(_params.pos_noaid_noise, lower_limit);
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R[3] = math::constrain(_gps_sample_delayed.hacc, lower_limit, upper_limit);
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_vel_pos_innov[3] = _state.pos(0) - _gps_sample_delayed.pos(0);
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_vel_pos_innov[4] = _state.pos(1) - _gps_sample_delayed.pos(1);
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// innovation gate size
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gate_size[3] = fmaxf(_params.posNE_innov_gate, 1.0f);
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} else if (_control_status.flags.ev_pos) {
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// we are using external vision measurements
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R[3] = fmaxf(_ev_sample_delayed.posErr, 0.01f);
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_vel_pos_innov[3] = _state.pos(0) - _ev_sample_delayed.posNED(0);
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_vel_pos_innov[4] = _state.pos(1) - _ev_sample_delayed.posNED(1);
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// innovation gate size
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gate_size[3] = fmaxf(_params.ev_innov_gate, 1.0f);
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} else {
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// No observations - use a static position to constrain drift
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if (_control_status.flags.in_air && _control_status.flags.tilt_align) {
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R[3] = fmaxf(_params.pos_noaid_noise, _params.gps_pos_noise);
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} else {
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R[3] = 0.5f;
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}
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_vel_pos_innov[3] = _state.pos(0) - _last_known_posNE(0);
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_vel_pos_innov[4] = _state.pos(1) - _last_known_posNE(1);
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// glitch protection is not required so set gate to a large value
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gate_size[3] = 100.0f;
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}
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// convert North position noise to variance
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R[3] = R[3] * R[3];
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// copy North axis values to East axis
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R[4] = R[3];
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gate_size[4] = gate_size[3];
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}
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if (_fuse_height) {
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if (_control_status.flags.baro_hgt) {
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fuse_map[5] = true;
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// vertical position innovation - baro measurement has opposite sign to earth z axis
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_vel_pos_innov[5] = _state.pos(2) + _baro_sample_delayed.hgt - _baro_hgt_offset - _hgt_sensor_offset;
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// observation variance - user parameter defined
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R[5] = fmaxf(_params.baro_noise, 0.01f);
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R[5] = R[5] * R[5];
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// innovation gate size
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gate_size[5] = fmaxf(_params.baro_innov_gate, 1.0f);
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} else if (_control_status.flags.gps_hgt) {
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fuse_map[5] = true;
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// vertical position innovation - gps measurement has opposite sign to earth z axis
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_vel_pos_innov[5] = _state.pos(2) + _gps_sample_delayed.hgt - _gps_alt_ref - _hgt_sensor_offset;
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// observation variance - receiver defined and parameter limited
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// use scaled horizontal position accuracy assuming typical ratio of VDOP/HDOP
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float lower_limit = fmaxf(_params.gps_pos_noise, 0.01f);
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float upper_limit = fmaxf(_params.pos_noaid_noise, lower_limit);
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R[5] = 1.5f * math::constrain(_gps_sample_delayed.vacc, lower_limit, upper_limit);
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R[5] = R[5] * R[5];
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// innovation gate size
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gate_size[5] = fmaxf(_params.baro_innov_gate, 1.0f);
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} else if (_control_status.flags.rng_hgt && (_R_to_earth(2, 2) > 0.7071f)) {
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fuse_map[5] = true;
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// use range finder with tilt correction
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_vel_pos_innov[5] = _state.pos(2) - (-math::max(_range_sample_delayed.rng * _R_to_earth(2, 2),
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_params.rng_gnd_clearance));
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// observation variance - user parameter defined
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R[5] = fmaxf(_params.range_noise, 0.01f);
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R[5] = R[5] * R[5];
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// innovation gate size
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gate_size[5] = fmaxf(_params.range_innov_gate, 1.0f);
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} else if (_control_status.flags.ev_hgt) {
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fuse_map[5] = true;
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// calculate the innovation assuming the external vision observaton is in local NED frame
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_vel_pos_innov[5] = _state.pos(2) - _ev_sample_delayed.posNED(2);
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// observation variance - defined externally
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R[5] = fmaxf(_ev_sample_delayed.posErr, 0.01f);
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R[5] = R[5] * R[5];
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// innovation gate size
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gate_size[5] = fmaxf(_params.ev_innov_gate, 1.0f);
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}
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}
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// calculate innovation test ratios
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for (unsigned obs_index = 0; obs_index < 6; obs_index++) {
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if (fuse_map[obs_index]) {
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// compute the innovation variance SK = HPH + R
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unsigned state_index = obs_index + 4; // we start with vx and this is the 4. state
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_vel_pos_innov_var[obs_index] = P[state_index][state_index] + R[obs_index];
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// Compute the ratio of innovation to gate size
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_vel_pos_test_ratio[obs_index] = sq(_vel_pos_innov[obs_index]) / (sq(gate_size[obs_index]) *
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_vel_pos_innov_var[obs_index]);
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}
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}
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// check position, velocity and height innovations
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// treat 3D velocity, 2D position and height as separate sensors
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// always pass position checks if using synthetic position measurements or yet to complete tilt alignment
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// always pass height checks if yet to complete tilt alignment
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bool vel_check_pass = (_vel_pos_test_ratio[0] <= 1.0f) && (_vel_pos_test_ratio[1] <= 1.0f)
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&& (_vel_pos_test_ratio[2] <= 1.0f);
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innov_check_pass_map[2] = innov_check_pass_map[1] = innov_check_pass_map[0] = vel_check_pass;
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bool pos_check_pass = ((_vel_pos_test_ratio[3] <= 1.0f) && (_vel_pos_test_ratio[4] <= 1.0f)) || !_control_status.flags.tilt_align;
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innov_check_pass_map[4] = innov_check_pass_map[3] = pos_check_pass;
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innov_check_pass_map[5] = (_vel_pos_test_ratio[5] <= 1.0f) || !_control_status.flags.tilt_align;
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// record the successful velocity fusion event
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if (vel_check_pass && _fuse_hor_vel) {
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_time_last_vel_fuse = _time_last_imu;
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_innov_check_fail_status.flags.reject_vel_NED = false;
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} else if (!vel_check_pass) {
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_innov_check_fail_status.flags.reject_vel_NED = true;
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}
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// record the successful position fusion event
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if (pos_check_pass && _fuse_pos) {
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_time_last_pos_fuse = _time_last_imu;
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_innov_check_fail_status.flags.reject_pos_NE = false;
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} else if (!pos_check_pass) {
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_innov_check_fail_status.flags.reject_pos_NE = true;
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}
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// record the successful height fusion event
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if (innov_check_pass_map[5] && _fuse_height) {
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_time_last_hgt_fuse = _time_last_imu;
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_innov_check_fail_status.flags.reject_pos_D = false;
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} else if (!innov_check_pass_map[5]) {
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_innov_check_fail_status.flags.reject_pos_D = true;
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}
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for (unsigned obs_index = 0; obs_index < 6; obs_index++) {
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// skip fusion if not requested or checks have failed
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if (!fuse_map[obs_index] || !innov_check_pass_map[obs_index]) {
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continue;
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}
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unsigned state_index = obs_index + 4; // we start with vx and this is the 4. state
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// calculate kalman gain K = PHS, where S = 1/innovation variance
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for (int row = 0; row < _k_num_states; row++) {
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Kfusion[row] = P[row][state_index] / _vel_pos_innov_var[obs_index];
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}
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// update covarinace matrix via Pnew = (I - KH)P
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float KHP[_k_num_states][_k_num_states];
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for (unsigned row = 0; row < _k_num_states; row++) {
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for (unsigned column = 0; column < _k_num_states; column++) {
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KHP[row][column] = Kfusion[row] * P[state_index][column];
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}
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}
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// if the covariance correction will result in a negative variance, then
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// the covariance marix is unhealthy and must be corrected
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bool healthy = true;
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for (int i = 0; i < _k_num_states; i++) {
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if (P[i][i] < KHP[i][i]) {
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// zero rows and columns
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zeroRows(P,i,i);
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zeroCols(P,i,i);
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//flag as unhealthy
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healthy = false;
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// update individual measurement health status
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if (obs_index == 0) {
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_fault_status.flags.bad_vel_N = true;
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} else if (obs_index == 1) {
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_fault_status.flags.bad_vel_E = true;
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} else if (obs_index == 2) {
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_fault_status.flags.bad_vel_D = true;
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} else if (obs_index == 3) {
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_fault_status.flags.bad_pos_N = true;
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} else if (obs_index == 4) {
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_fault_status.flags.bad_pos_E = true;
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} else if (obs_index == 5) {
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_fault_status.flags.bad_pos_D = true;
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}
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} else {
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// update individual measurement health status
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if (obs_index == 0) {
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_fault_status.flags.bad_vel_N = false;
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} else if (obs_index == 1) {
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_fault_status.flags.bad_vel_E = false;
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} else if (obs_index == 2) {
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_fault_status.flags.bad_vel_D = false;
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} else if (obs_index == 3) {
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_fault_status.flags.bad_pos_N = false;
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} else if (obs_index == 4) {
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_fault_status.flags.bad_pos_E = false;
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} else if (obs_index == 5) {
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_fault_status.flags.bad_pos_D = false;
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}
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}
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}
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// only apply covariance and state corrrections if healthy
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if (healthy) {
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// apply the covariance corrections
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for (unsigned row = 0; row < _k_num_states; row++) {
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for (unsigned column = 0; column < _k_num_states; column++) {
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P[row][column] = P[row][column] - KHP[row][column];
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}
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}
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// correct the covariance marix for gross errors
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fixCovarianceErrors();
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// apply the state corrections
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fuse(Kfusion, _vel_pos_innov[obs_index]);
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}
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}
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}
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