mirror of https://github.com/ArduPilot/ardupilot
473 lines
16 KiB
C++
473 lines
16 KiB
C++
/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-
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/*
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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// Authored by Jonathan Challinger, 3D Robotics Inc.
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#include "AccelCalibrator.h"
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#include <stdio.h>
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#include <AP_HAL/AP_HAL.h>
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const extern AP_HAL::HAL& hal;
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/*
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* TODO
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* - time out when not receiving samples
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*/
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////////////////////////////////////////////////////////////
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///////////////////// PUBLIC INTERFACE /////////////////////
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////////////////////////////////////////////////////////////
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AccelCalibrator::AccelCalibrator() :
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_conf_tolerance(ACCEL_CAL_TOLERANCE),
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_sample_buffer(NULL)
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{
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clear();
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}
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/*
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Select options, initialise variables and initiate accel calibration
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Options:
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Fit Type: Will assume that if accelerometer static samples around all possible orientatio
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are spread in space will cover a surface of AXIS_ALIGNED_ELLIPSOID or any general
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ELLIPSOID as chosen
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Num Samples: Number of samples user should will be gathering, please note that with type of surface
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chosen the minimum number of samples required will vary, for instance in the case of AXIS
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ALIGNED ELLIPSOID atleast 6 distinct samples are required while for generic ELLIPSOIDAL fit
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which will include calculation of offdiagonal parameters too requires atleast 8 parameters
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to form a distinct ELLIPSOID
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Sample Time: Time over which the samples will be gathered and averaged to add to a single sample for least
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square regression
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offset,diag,offdiag: initial parameter values for LSQ calculation
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*/
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void AccelCalibrator::start(enum accel_cal_fit_type_t fit_type, uint8_t num_samples, float sample_time) {
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start(fit_type, num_samples, sample_time, Vector3f(0,0,0), Vector3f(1,1,1), Vector3f(0,0,0));
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}
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void AccelCalibrator::start(enum accel_cal_fit_type_t fit_type, uint8_t num_samples, float sample_time, Vector3f offset, Vector3f diag, Vector3f offdiag) {
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if (_status == ACCEL_CAL_FAILED || _status == ACCEL_CAL_SUCCESS) {
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clear();
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}
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if (_status != ACCEL_CAL_NOT_STARTED) {
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return;
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}
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_conf_num_samples = num_samples;
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_conf_sample_time = sample_time;
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_conf_fit_type = fit_type;
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const uint16_t faces = 2*_conf_num_samples-4;
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const float a = (4.0f * M_PI_F / (3.0f * faces)) + M_PI_F / 3.0f;
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const float theta = 0.5f * acosf(cosf(a) / (1.0f - cosf(a)));
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_min_sample_dist = GRAVITY_MSS * 2*sinf(theta/2);
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_param.s.offset = offset;
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_param.s.diag = diag;
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_param.s.offdiag = offdiag;
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switch (_conf_fit_type) {
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case ACCEL_CAL_AXIS_ALIGNED_ELLIPSOID:
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if (_conf_num_samples < 6) {
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set_status(ACCEL_CAL_FAILED);
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return;
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}
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break;
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case ACCEL_CAL_ELLIPSOID:
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if (_conf_num_samples < 8) {
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set_status(ACCEL_CAL_FAILED);
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return;
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}
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break;
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}
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set_status(ACCEL_CAL_WAITING_FOR_ORIENTATION);
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}
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// set Accel calibrator status to make itself ready for future accel cals
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void AccelCalibrator::clear() {
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set_status(ACCEL_CAL_NOT_STARTED);
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}
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// returns true if accel calibrator is running
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bool AccelCalibrator::running() {
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return _status == ACCEL_CAL_WAITING_FOR_ORIENTATION || _status == ACCEL_CAL_COLLECTING_SAMPLE;
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}
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// set Accel calibrator to start collecting samples in the next cycle
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void AccelCalibrator::collect_sample() {
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set_status(ACCEL_CAL_COLLECTING_SAMPLE);
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}
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// collect and avg sample to be passed onto LSQ estimator after all requisite orientations are done
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void AccelCalibrator::new_sample(const Vector3f& delta_velocity, float dt) {
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if (_status != ACCEL_CAL_COLLECTING_SAMPLE) {
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return;
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}
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if (_samples_collected >= _conf_num_samples) {
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set_status(ACCEL_CAL_FAILED);
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return;
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}
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_sample_buffer[_samples_collected].delta_velocity += delta_velocity;
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_sample_buffer[_samples_collected].delta_time += dt;
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_last_samp_frag_collected_ms = AP_HAL::millis();
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if (_sample_buffer[_samples_collected].delta_time > _conf_sample_time) {
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Vector3f sample = _sample_buffer[_samples_collected].delta_velocity/_sample_buffer[_samples_collected].delta_time;
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if (!accept_sample(sample)) {
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set_status(ACCEL_CAL_FAILED);
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return;
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}
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_samples_collected++;
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if (_samples_collected >= _conf_num_samples) {
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run_fit(MAX_ITERATIONS, _fitness);
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if (_fitness < _conf_tolerance && accept_result()) {
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set_status(ACCEL_CAL_SUCCESS);
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} else {
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set_status(ACCEL_CAL_FAILED);
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}
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} else {
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set_status(ACCEL_CAL_WAITING_FOR_ORIENTATION);
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}
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}
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}
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// determines if the result is acceptable
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bool AccelCalibrator::accept_result() const {
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if (fabsf(_param.s.offset.x) > GRAVITY_MSS ||
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fabsf(_param.s.offset.y) > GRAVITY_MSS ||
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fabsf(_param.s.offset.z) > GRAVITY_MSS ||
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_param.s.diag.x < 0.8f || _param.s.diag.x > 1.2f ||
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_param.s.diag.y < 0.8f || _param.s.diag.y > 1.2f ||
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_param.s.diag.z < 0.8f || _param.s.diag.z > 1.2f) {
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return false;
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} else {
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return true;
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}
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}
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// interface for LSq estimator to read sample buffer sent after conversion from delta velocity
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// to averaged acc over time
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bool AccelCalibrator::get_sample(uint8_t i, Vector3f& s) const {
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if (i >= _samples_collected) {
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return false;
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}
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s = _sample_buffer[i].delta_velocity / _sample_buffer[i].delta_time;
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return true;
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}
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// returns truen and sample corrected with diag offdiag parameters as calculated by LSq estimation procedure
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// returns false if no correct parameter exists to be applied along with existing sample without corrections
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bool AccelCalibrator::get_sample_corrected(uint8_t i, Vector3f& s) const {
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if (_status != ACCEL_CAL_SUCCESS || !get_sample(i,s)) {
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return false;
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}
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Matrix3f M (
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_param.s.diag.x , _param.s.offdiag.x , _param.s.offdiag.y,
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_param.s.offdiag.x , _param.s.diag.y , _param.s.offdiag.z,
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_param.s.offdiag.y , _param.s.offdiag.z , _param.s.diag.z
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);
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s = M*(s+_param.s.offset);
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return true;
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}
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// checks if no new sample has been recieved for considerable amount of time
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void AccelCalibrator::check_for_timeout() {
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const uint32_t timeout = _conf_sample_time*2*1000 + 500;
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if (_status == ACCEL_CAL_COLLECTING_SAMPLE && AP_HAL::millis() - _last_samp_frag_collected_ms > timeout) {
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set_status(ACCEL_CAL_FAILED);
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}
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}
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// returns spherical fit paramteters
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void AccelCalibrator::get_calibration(Vector3f& offset) const {
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offset = -_param.s.offset;
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}
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// returns axis aligned ellipsoidal fit parameters
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void AccelCalibrator::get_calibration(Vector3f& offset, Vector3f& diag) const {
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offset = -_param.s.offset;
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diag = _param.s.diag;
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}
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// returns generic ellipsoidal fit parameters
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void AccelCalibrator::get_calibration(Vector3f& offset, Vector3f& diag, Vector3f& offdiag) const {
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offset = -_param.s.offset;
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diag = _param.s.diag;
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offdiag = _param.s.offdiag;
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}
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/////////////////////////////////////////////////////////////
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////////////////////// PRIVATE METHODS //////////////////////
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/////////////////////////////////////////////////////////////
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/*
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The sample acceptance distance is determined as follows:
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For any regular polyhedron with triangular faces, the angle theta subtended
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by two closest points is defined as
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theta = arccos(cos(A)/(1-cos(A)))
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Where:
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A = (4pi/F + pi)/3
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and
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F = 2V - 4 is the number of faces for the polyhedron in consideration,
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which depends on the number of vertices V
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The above equation was proved after solving for spherical triangular excess
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and related equations.
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*/
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bool AccelCalibrator::accept_sample(const Vector3f& sample)
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{
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if (_sample_buffer == NULL) {
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return false;
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}
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for(uint8_t i=0; i < _samples_collected; i++) {
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Vector3f other_sample;
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get_sample(i, other_sample);
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if ((other_sample - sample).length() < _min_sample_dist){
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return false;
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}
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}
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return true;
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}
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// sets status of calibrator and takes appropriate actions
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void AccelCalibrator::set_status(enum accel_cal_status_t status) {
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switch (status) {
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case ACCEL_CAL_NOT_STARTED:
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//Calibrator not started
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_status = ACCEL_CAL_NOT_STARTED;
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_samples_collected = 0;
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if (_sample_buffer != NULL) {
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free(_sample_buffer);
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_sample_buffer = NULL;
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}
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break;
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case ACCEL_CAL_WAITING_FOR_ORIENTATION:
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//Callibrator has been started and is waiting for user to ack after orientation setting
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if (!running()) {
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_samples_collected = 0;
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if (_sample_buffer == NULL) {
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_sample_buffer = (struct AccelSample*)calloc(_conf_num_samples,sizeof(struct AccelSample));
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if (_sample_buffer == NULL) {
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set_status(ACCEL_CAL_FAILED);
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break;
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}
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}
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}
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if (_samples_collected >= _conf_num_samples) {
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break;
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}
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_status = ACCEL_CAL_WAITING_FOR_ORIENTATION;
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break;
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case ACCEL_CAL_COLLECTING_SAMPLE:
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// Calibrator is waiting on collecting samples from acceleromter for amount of
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// time as requested by user/GCS
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if (_status != ACCEL_CAL_WAITING_FOR_ORIENTATION) {
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break;
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}
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_last_samp_frag_collected_ms = AP_HAL::millis();
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_status = ACCEL_CAL_COLLECTING_SAMPLE;
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break;
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case ACCEL_CAL_SUCCESS:
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// Calibrator has successfully fitted the samples to user requested surface model
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// and has passed tolerance test
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if (_status != ACCEL_CAL_COLLECTING_SAMPLE) {
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break;
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}
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_status = ACCEL_CAL_SUCCESS;
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break;
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case ACCEL_CAL_FAILED:
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// Calibration has failed with reasons that can range from
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// bad sample data leading to faillure in tolerance test to lack of distinct samples
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if (_status == ACCEL_CAL_NOT_STARTED) {
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break;
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}
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_status = ACCEL_CAL_FAILED;
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break;
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};
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}
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/*
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Run Gauss Newton fitting algorithm over the sample space and come up with offsets, diagonal/scale factors
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and crosstalk/offdiagonal parameters
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*/
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void AccelCalibrator::run_fit(uint8_t max_iterations, float& fitness)
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{
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if (_sample_buffer == NULL) {
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return;
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}
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fitness = calc_mean_squared_residuals(_param.s);
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float min_fitness = fitness;
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union param_u fit_param = _param;
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uint8_t num_iterations = 0;
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while(num_iterations < max_iterations) {
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float last_fitness = fitness;
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float JTJ[ACCEL_CAL_MAX_NUM_PARAMS*ACCEL_CAL_MAX_NUM_PARAMS] {};
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VectorP JTFI;
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for(uint16_t k = 0; k<_samples_collected; k++) {
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Vector3f sample;
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get_sample(k, sample);
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VectorN<float,ACCEL_CAL_MAX_NUM_PARAMS> jacob;
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calc_jacob(sample, fit_param.s, jacob);
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for(uint8_t i = 0; i < get_num_params(); i++) {
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// compute JTJ
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for(uint8_t j = 0; j < get_num_params(); j++) {
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JTJ[i*get_num_params()+j] += jacob[i] * jacob[j];
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}
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// compute JTFI
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JTFI[i] += jacob[i] * calc_residual(sample, fit_param.s);
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}
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}
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if (!inverse(JTJ, JTJ, get_num_params())) {
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return;
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}
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for(uint8_t row=0; row < get_num_params(); row++) {
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for(uint8_t col=0; col < get_num_params(); col++) {
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fit_param.a[row] -= JTFI[col] * JTJ[row*get_num_params()+col];
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}
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}
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fitness = calc_mean_squared_residuals(fit_param.s);
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if (isnan(fitness) || isinf(fitness)) {
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return;
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}
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if (fitness < min_fitness) {
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min_fitness = fitness;
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_param = fit_param;
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}
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num_iterations++;
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if (fitness - last_fitness < 1.0e-9f) {
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break;
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}
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}
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}
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// calculates residual from samples(corrected using supplied parameter) to gravity
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// used to create Fitness column matrix
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float AccelCalibrator::calc_residual(const Vector3f& sample, const struct param_t& params) const {
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Matrix3f M (
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params.diag.x , params.offdiag.x , params.offdiag.y,
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params.offdiag.x , params.diag.y , params.offdiag.z,
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params.offdiag.y , params.offdiag.z , params.diag.z
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);
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return GRAVITY_MSS - (M*(sample+params.offset)).length();
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}
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// calculated the total mean squared fitness of all the collected samples using parameters
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// converged to LSq estimator so far
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float AccelCalibrator::calc_mean_squared_residuals() const
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{
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return calc_mean_squared_residuals(_param.s);
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}
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// calculated the total mean squared fitness of all the collected samples using parameters
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// supplied
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float AccelCalibrator::calc_mean_squared_residuals(const struct param_t& params) const
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{
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if (_sample_buffer == NULL || _samples_collected == 0) {
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return 1.0e30f;
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}
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float sum = 0.0f;
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for(uint16_t i=0; i < _samples_collected; i++){
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Vector3f sample;
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get_sample(i, sample);
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float resid = calc_residual(sample, params);
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sum += sq(resid);
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}
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sum /= _samples_collected;
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return sum;
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}
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// calculate jacobian, a matrix that defines relation to variation in fitness with variation in each of the parameters
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// this is used in LSq estimator to adjust variation in parameter to be used for next iteration of LSq
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void AccelCalibrator::calc_jacob(const Vector3f& sample, const struct param_t& params, VectorP &ret) const {
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switch (_conf_fit_type) {
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case ACCEL_CAL_AXIS_ALIGNED_ELLIPSOID:
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case ACCEL_CAL_ELLIPSOID: {
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const Vector3f &offset = params.offset;
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const Vector3f &diag = params.diag;
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const Vector3f &offdiag = params.offdiag;
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Matrix3f M(
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diag.x , offdiag.x , offdiag.y,
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offdiag.x , diag.y , offdiag.z,
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offdiag.y , offdiag.z , diag.z
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);
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float A = (diag.x * (sample.x + offset.x)) + (offdiag.x * (sample.y + offset.y)) + (offdiag.y * (sample.z + offset.z));
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float B = (offdiag.x * (sample.x + offset.x)) + (diag.y * (sample.y + offset.y)) + (offdiag.z * (sample.z + offset.z));
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float C = (offdiag.y * (sample.x + offset.x)) + (offdiag.z * (sample.y + offset.y)) + (diag.z * (sample.z + offset.z));
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float length = (M*(sample+offset)).length();
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// 0-2: offsets
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ret[0] = -1.0f * (((diag.x * A) + (offdiag.x * B) + (offdiag.y * C))/length);
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ret[1] = -1.0f * (((offdiag.x * A) + (diag.y * B) + (offdiag.z * C))/length);
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ret[2] = -1.0f * (((offdiag.y * A) + (offdiag.z * B) + (diag.z * C))/length);
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// 3-5: diagonals
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ret[3] = -1.0f * ((sample.x + offset.x) * A)/length;
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ret[4] = -1.0f * ((sample.y + offset.y) * B)/length;
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ret[5] = -1.0f * ((sample.z + offset.z) * C)/length;
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// 6-8: off-diagonals
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ret[6] = -1.0f * (((sample.y + offset.y) * A) + ((sample.x + offset.x) * B))/length;
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ret[7] = -1.0f * (((sample.z + offset.z) * A) + ((sample.x + offset.x) * C))/length;
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ret[8] = -1.0f * (((sample.z + offset.z) * B) + ((sample.y + offset.y) * C))/length;
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return;
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}
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};
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}
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// returns number of paramters are required for selected Fit type
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uint8_t AccelCalibrator::get_num_params() const {
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switch (_conf_fit_type) {
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case ACCEL_CAL_AXIS_ALIGNED_ELLIPSOID:
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return 6;
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case ACCEL_CAL_ELLIPSOID:
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return 9;
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default:
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return 6;
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}
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}
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