forked from Archive/PX4-Autopilot
159 lines
5.7 KiB
C++
159 lines
5.7 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 terrain_estimator.cpp
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* Function for fusing rangefinder measurements to estimate terrain vertical position/
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*
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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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bool Ekf::initHagl()
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{
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// get most recent range measurement from buffer
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rangeSample latest_measurement = _range_buffer.get_newest();
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if ((_time_last_imu - latest_measurement.time_us) < 2e5) {
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// if we have a fresh measurement, use it to initialise the terrain estimator
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_terrain_vpos = _state.pos(2) + latest_measurement.rng;
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// initialise state variance to variance of measurement
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_terrain_var = sq(_params.range_noise);
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// success
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return true;
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} else if (!_control_status.flags.in_air) {
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// if on ground we assume a ground clearance
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_terrain_vpos = _state.pos(2) + _params.rng_gnd_clearance;
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// Use the ground clearance value as our uncertainty
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_terrain_var = sq(_params.rng_gnd_clearance);
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// ths is a guess
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return false;
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} else {
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// no information - cannot initialise
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return false;
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}
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}
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void Ekf::predictHagl()
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{
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// predict the state variance growth
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// the state is the vertical position of the terrain underneath the vehicle
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// process noise due to errors in vehicle height estimate
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_terrain_var += sq(_imu_sample_delayed.delta_vel_dt * _params.terrain_p_noise);
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// process noise due to terrain gradient
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_terrain_var += sq(_imu_sample_delayed.delta_vel_dt * _params.terrain_gradient) * (sq(_state.vel(0)) + sq(_state.vel(
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1)));
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// limit the variance to prevent it becoming badly conditioned
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_terrain_var = math::constrain(_terrain_var, 0.0f, 1e4f);
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}
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void Ekf::fuseHagl()
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{
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// If the vehicle is excessively tilted, do not try to fuse range finder observations
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if (_R_to_earth(2, 2) > 0.7071f) {
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// get a height above ground measurement from the range finder assuming a flat earth
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float meas_hagl = _range_sample_delayed.rng * _R_to_earth(2, 2);
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// predict the hagl from the vehicle position and terrain height
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float pred_hagl = _terrain_vpos - _state.pos(2);
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// calculate the innovation
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_hagl_innov = pred_hagl - meas_hagl;
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// calculate the observation variance adding the variance of the vehicles own height uncertainty and factoring in the effect of tilt on measurement error
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float obs_variance = fmaxf(P[9][9], 0.0f) + sq(_params.range_noise / _R_to_earth(2, 2));
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// calculate the innovation variance - limiting it to prevent a badly conditioned fusion
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_hagl_innov_var = fmaxf(_terrain_var + obs_variance, obs_variance);
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// perform an innovation consistency check and only fuse data if it passes
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float gate_size = fmaxf(_params.range_innov_gate, 1.0f);
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float test_ratio = sq(_hagl_innov) / (sq(gate_size) * _hagl_innov_var);
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if (test_ratio <= 1.0f) {
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// calculate the Kalman gain
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float gain = _terrain_var / _hagl_innov_var;
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// correct the state
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_terrain_vpos -= gain * _hagl_innov;
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// correct the variance
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_terrain_var = fmaxf(_terrain_var * (1.0f - gain), 0.0f);
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// record last successful fusion event
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_time_last_hagl_fuse = _time_last_imu;
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_sensor_fault_status.flags.reject_hagl = false;
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} else {
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_sensor_fault_status.flags.reject_hagl = true;
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}
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} else {
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return;
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}
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}
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// return true if the estimate is fresh
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// return the estimated vertical position of the terrain relative to the NED origin
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bool Ekf::get_terrain_vert_pos(float *ret)
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{
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memcpy(ret, &_terrain_vpos, sizeof(float));
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// The height is useful if the uncertainty in terrain height is significantly smaller than than the estimated height above terrain
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bool accuracy_useful = (sqrtf(_terrain_var) < 0.2f * fmaxf((_terrain_vpos - _state.pos(2)), _params.rng_gnd_clearance));
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if (_time_last_imu - _time_last_hagl_fuse < 1e6 || accuracy_useful) {
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return true;
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} else {
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return false;
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}
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}
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void Ekf::get_hagl_innov(float *hagl_innov)
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{
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memcpy(hagl_innov, &_hagl_innov, sizeof(_hagl_innov));
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}
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void Ekf::get_hagl_innov_var(float *hagl_innov_var)
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{
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memcpy(hagl_innov_var, &_hagl_innov_var, sizeof(_hagl_innov_var));
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}
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