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305 lines
8.4 KiB
C++
305 lines
8.4 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2021 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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#include "MagBiasEstimator.hpp"
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using namespace time_literals;
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using matrix::Vector3f;
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namespace mag_bias_estimator
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{
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MagBiasEstimator::MagBiasEstimator() :
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ModuleParams(nullptr),
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ScheduledWorkItem(MODULE_NAME, px4::wq_configurations::lp_default)
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{
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_magnetometer_bias_estimate_pub.advertise();
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}
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MagBiasEstimator::~MagBiasEstimator()
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{
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perf_free(_cycle_perf);
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}
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int MagBiasEstimator::task_spawn(int argc, char *argv[])
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{
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MagBiasEstimator *obj = new MagBiasEstimator();
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if (!obj) {
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PX4_ERR("alloc failed");
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return -1;
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}
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_object.store(obj);
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_task_id = task_id_is_work_queue;
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/* Schedule a cycle to start things. */
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obj->start();
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return 0;
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}
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void MagBiasEstimator::start()
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{
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ScheduleOnInterval(20_ms); // 50 Hz
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}
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void MagBiasEstimator::Run()
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{
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if (should_exit()) {
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ScheduleClear();
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exit_and_cleanup();
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}
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if (_vehicle_status_sub.updated()) {
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vehicle_status_s vehicle_status;
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if (_vehicle_status_sub.copy(&vehicle_status)) {
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if (_arming_state != vehicle_status.arming_state) {
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_arming_state = vehicle_status.arming_state;
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// reset on any arming state change
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for (auto &reset : _reset_field_estimator) {
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reset = true;
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}
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if (_arming_state == vehicle_status_s::ARMING_STATE_ARMED) {
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ScheduleOnInterval(1_s);
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} else {
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// restore 50 Hz scheduling
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ScheduleOnInterval(20_ms);
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}
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}
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}
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}
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// only run when disarmed
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if (_arming_state == vehicle_status_s::ARMING_STATE_ARMED) {
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return;
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}
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// check for parameter updates
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if (_parameter_update_sub.updated()) {
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// clear update
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parameter_update_s pupdate;
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_parameter_update_sub.copy(&pupdate);
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// update parameters from storage
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updateParams();
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for (int mag_index = 0; mag_index < MAX_SENSOR_COUNT; mag_index++) {
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const auto calibration_count = _calibration[mag_index].calibration_count();
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_calibration[mag_index].ParametersUpdate();
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if (calibration_count != _calibration[mag_index].calibration_count()) {
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_reset_field_estimator[mag_index] = true;
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}
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_bias_estimator[mag_index].setLearningGain(_param_mbe_learn_gain.get());
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}
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}
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if (_vehicle_status_flags_sub.updated()) {
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vehicle_status_flags_s vehicle_status_flags;
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if (_vehicle_status_flags_sub.copy(&vehicle_status_flags)) {
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bool system_calibrating = vehicle_status_flags.calibration_enabled;
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if (system_calibrating != _system_calibrating) {
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_system_calibrating = system_calibrating;
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for (auto &reset : _reset_field_estimator) {
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reset = true;
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}
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}
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}
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}
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// do nothing during regular sensor calibration
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if (_system_calibrating) {
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return;
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}
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perf_begin(_cycle_perf);
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// Assume a constant angular velocity during two mag samples
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vehicle_angular_velocity_s vehicle_angular_velocity;
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if (_vehicle_angular_velocity_sub.update(&vehicle_angular_velocity)) {
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const Vector3f angular_velocity{vehicle_angular_velocity.xyz};
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bool updated = false;
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for (int mag_index = 0; mag_index < MAX_SENSOR_COUNT; mag_index++) {
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sensor_mag_s sensor_mag;
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while (_sensor_mag_subs[mag_index].update(&sensor_mag)) {
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updated = true;
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// apply existing mag calibration
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_calibration[mag_index].set_device_id(sensor_mag.device_id);
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const Vector3f mag_calibrated = _calibration[mag_index].Correct(Vector3f{sensor_mag.x, sensor_mag.y, sensor_mag.z});
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float dt = (sensor_mag.timestamp_sample - _timestamp_last_update[mag_index]) * 1e-6f;
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_timestamp_last_update[mag_index] = sensor_mag.timestamp_sample;
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if (dt < 0.001f || dt > 0.2f) {
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_reset_field_estimator[mag_index] = true;
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}
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if (_reset_field_estimator[mag_index]) {
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// reset
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_bias_estimator[mag_index].setBias(Vector3f{});
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_bias_estimator[mag_index].setField(mag_calibrated);
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_reset_field_estimator[mag_index] = false;
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_valid[mag_index] = false;
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_time_valid[mag_index] = 0;
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} else {
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updated = true;
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const Vector3f bias_prev = _bias_estimator[mag_index].getBias();
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_bias_estimator[mag_index].updateEstimate(angular_velocity, mag_calibrated, dt);
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const Vector3f &bias = _bias_estimator[mag_index].getBias();
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const Vector3f bias_rate = (bias - bias_prev) / dt;
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if (!PX4_ISFINITE(bias(0)) || !PX4_ISFINITE(bias(1)) || !PX4_ISFINITE(bias(2)) || bias.longerThan(5.f)) {
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_reset_field_estimator[mag_index] = true;
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_valid[mag_index] = false;
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_time_valid[mag_index] = 0;
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} else {
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Vector3f fitness{
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fabsf(angular_velocity(0)) / fmaxf(fabsf(bias_rate(1)) + fabsf(bias_rate(2)), 0.02f),
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fabsf(angular_velocity(1)) / fmaxf(fabsf(bias_rate(0)) + fabsf(bias_rate(2)), 0.02f),
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fabsf(angular_velocity(2)) / fmaxf(fabsf(bias_rate(0)) + fabsf(bias_rate(1)), 0.02f)
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};
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const bool bias_significant = bias.longerThan(0.04f);
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const bool has_converged = fitness(0) > 20.f || fitness(1) > 20.f || fitness(2) > 20.f;
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if (bias_significant && has_converged) {
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if (!_valid[mag_index]) {
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_time_valid[mag_index] = hrt_absolute_time();
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}
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_valid[mag_index] = true;
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}
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}
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}
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}
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}
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if (updated) {
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publishMagBiasEstimate();
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}
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}
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perf_end(_cycle_perf);
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}
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void MagBiasEstimator::publishMagBiasEstimate()
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{
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magnetometer_bias_estimate_s mag_bias_est{};
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for (int mag_index = 0; mag_index < MAX_SENSOR_COUNT; mag_index++) {
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const Vector3f &bias = _bias_estimator[mag_index].getBias();
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mag_bias_est.bias_x[mag_index] = bias(0);
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mag_bias_est.bias_y[mag_index] = bias(1);
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mag_bias_est.bias_z[mag_index] = bias(2);
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mag_bias_est.valid[mag_index] = _valid[mag_index];
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if (_valid[mag_index]) {
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mag_bias_est.valid[mag_index] = true;
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mag_bias_est.stable[mag_index] = (hrt_elapsed_time(&_time_valid[mag_index]) > 30_s);
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}
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}
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mag_bias_est.timestamp = hrt_absolute_time();
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_magnetometer_bias_estimate_pub.publish(mag_bias_est);
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}
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int MagBiasEstimator::print_status()
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{
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for (int mag_index = 0; mag_index < MAX_SENSOR_COUNT; mag_index++) {
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if (_calibration[mag_index].device_id() != 0) {
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_calibration[mag_index].PrintStatus();
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const Vector3f &bias = _bias_estimator[mag_index].getBias();
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PX4_INFO("%d (%" PRIu32 ") bias: [% 05.3f % 05.3f % 05.3f]",
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mag_index, _calibration[mag_index].device_id(),
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(double)bias(0),
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(double)bias(1),
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(double)bias(2));
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}
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}
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return 0;
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}
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int MagBiasEstimator::print_usage(const char *reason)
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{
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if (reason) {
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PX4_ERR("%s\n", reason);
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}
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PRINT_MODULE_DESCRIPTION(
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R"DESCR_STR(
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### Description
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Online magnetometer bias estimator.
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)DESCR_STR");
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PRINT_MODULE_USAGE_NAME("mag_bias_estimator", "system");
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PRINT_MODULE_USAGE_COMMAND_DESCR("start", "Start the background task");
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PRINT_MODULE_USAGE_DEFAULT_COMMANDS();
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return 0;
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}
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extern "C" __EXPORT int mag_bias_estimator_main(int argc, char *argv[])
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{
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return MagBiasEstimator::main(argc, argv);
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}
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} // namespace load_mon
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