mirror of https://github.com/ArduPilot/ardupilot
AP_OpticalFlow: add calibrator
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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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#include "AP_OpticalFlow_Calibrator.h"
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#include <GCS_MAVLink/GCS.h>
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#include <AP_Logger/AP_Logger.h>
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const uint32_t AP_OPTICALFLOW_CAL_TIMEOUT_SEC = 120; // calibration timesout after 120 seconds
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const uint32_t AP_OPTICALFLOW_CAL_STATUSINTERVAL_SEC = 3; // status updates printed at 3 second intervals
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const float AP_OPTICALFLOW_CAL_YAW_MAX_RADS = radians(20); // maximum yaw rotation (must be low to ensure good scaling)
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const float AP_OPTICALFLOW_CAL_ROLLPITCH_MIN_RADS = radians(20); // minimum acceptable roll or pitch rotation
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const float AP_OPTICALFLOW_CAL_SCALE_MIN = 0.20f; // min acceptable scaling value. If resulting scaling is below this then the calibration fails
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const float AP_OPTICALFLOW_CAL_SCALE_MAX = 4.0f; // max acceptable scaling value. If resulting scaling is above this then the calibration fails
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const float AP_OPTICALFLOW_CAL_FITNESS_THRESH = 0.5f; // min acceptable fitness
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const float AP_OPTICALFLOW_CAL_RMS_FAILED = 1.0e30f; // calc_mean_squared_residuals returns this value when it fails to calculate a good value
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extern const AP_HAL::HAL& hal;
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// start the calibration
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void AP_OpticalFlow_Calibrator::start()
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{
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// exit immediately if already running
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if (_cal_state == CalState::RUNNING) {
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return;
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}
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_cal_state = CalState::RUNNING;
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_start_time_ms = AP_HAL::millis();
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// clear samples buffers
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_cal_data[0].num_samples = 0;
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_cal_data[1].num_samples = 0;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "FlowCal: Started");
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}
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void AP_OpticalFlow_Calibrator::stop()
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{
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// exit immediately if already stopped
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if (_cal_state == CalState::NOT_STARTED) {
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return;
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}
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_cal_state = CalState::NOT_STARTED;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "FlowCal: Stopped");
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}
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// update the state machine and calculate scaling
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bool AP_OpticalFlow_Calibrator::update()
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{
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// prefix for reporting
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const char* prefix_str = "FlowCal:";
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// while running add samples
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if (_cal_state == CalState::RUNNING) {
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uint32_t now_ms = AP_HAL::millis();
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uint32_t timestamp_ms;
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Vector2f flow_rate, body_rate, los_pred;
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if (AP::ahrs().getOptFlowSample(timestamp_ms, flow_rate, body_rate, los_pred)) {
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add_sample(timestamp_ms, flow_rate, body_rate, los_pred);
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// while collecting samples display percentage complete
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if (now_ms - _last_report_ms > AP_OPTICALFLOW_CAL_STATUSINTERVAL_SEC * 1000UL) {
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_last_report_ms = now_ms;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s x:%d%% y:%d%%",
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prefix_str,
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(int)((_cal_data[0].num_samples * 100.0 / AP_OPTICALFLOW_CAL_MAX_SAMPLES)),
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(int)((_cal_data[1].num_samples * 100.0 / AP_OPTICALFLOW_CAL_MAX_SAMPLES)));
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}
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// advance state once sample buffers are full
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if (sample_buffers_full()) {
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_cal_state = CalState::READY_TO_CALIBRATE;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s samples collected", prefix_str);
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}
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}
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// check for timeout
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if (now_ms - _start_time_ms > AP_OPTICALFLOW_CAL_TIMEOUT_SEC * 1000UL) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s timeout", prefix_str);
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_cal_state = CalState::FAILED;
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}
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}
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// start calibration
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if (_cal_state == CalState::READY_TO_CALIBRATE) {
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// run calibration and mark failure or success
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if (run_calibration()) {
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_cal_state = CalState::SUCCESS;
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return true;
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} else {
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_cal_state = CalState::FAILED;
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}
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}
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// return indicating calibration is not complete
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return false;
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}
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// get final scaling values
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// scaling values used during sample collection should be multiplied by these scalars
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Vector2f AP_OpticalFlow_Calibrator::get_scalars()
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{
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// return best scaling values
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return Vector2f{_cal_data[0].best_scalar, _cal_data[1].best_scalar};
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}
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// add new sample to the calibrator
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void AP_OpticalFlow_Calibrator::add_sample(uint32_t timestamp_ms, const Vector2f& flow_rate, const Vector2f& body_rate, const Vector2f& los_pred)
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{
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// return immediately if not running
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if (_cal_state != CalState::RUNNING) {
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return;
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}
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// check for duplicates
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if (timestamp_ms == _last_sample_timestamp_ms) {
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return;
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}
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_last_sample_timestamp_ms = timestamp_ms;
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// check yaw rotation is low
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const Vector3f gyro = AP::ahrs().get_gyro();
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if (fabsf(gyro.z) > AP_OPTICALFLOW_CAL_YAW_MAX_RADS) {
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return;
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}
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// check enough roll or pitch movement and record sample
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const bool rates_x_sufficient = (fabsf(body_rate.x) >= AP_OPTICALFLOW_CAL_ROLLPITCH_MIN_RADS) && (fabsf(flow_rate.x) >= AP_OPTICALFLOW_CAL_ROLLPITCH_MIN_RADS);
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if (rates_x_sufficient && (_cal_data[0].num_samples < ARRAY_SIZE(_cal_data[0].samples))) {
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log_sample(0, _cal_data[0].num_samples, flow_rate.x, body_rate.x, los_pred.x);
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_cal_data[0].samples[_cal_data[0].num_samples].flow_rate = flow_rate.x;
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_cal_data[0].samples[_cal_data[0].num_samples].body_rate = body_rate.x;
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_cal_data[0].samples[_cal_data[0].num_samples].los_pred = los_pred.x;
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_cal_data[0].num_samples++;
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}
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const bool rates_y_sufficient = (fabsf(body_rate.y) >= AP_OPTICALFLOW_CAL_ROLLPITCH_MIN_RADS) && (fabsf(flow_rate.y) >= AP_OPTICALFLOW_CAL_ROLLPITCH_MIN_RADS);
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if (rates_y_sufficient && (_cal_data[1].num_samples < ARRAY_SIZE(_cal_data[1].samples))) {
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log_sample(1, _cal_data[1].num_samples, flow_rate.y, body_rate.y, los_pred.y);
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_cal_data[1].samples[_cal_data[1].num_samples].flow_rate = flow_rate.y;
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_cal_data[1].samples[_cal_data[1].num_samples].body_rate = body_rate.y;
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_cal_data[1].samples[_cal_data[1].num_samples].los_pred = los_pred.y;
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_cal_data[1].num_samples++;
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}
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}
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// returns true once the sample buffer is full
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bool AP_OpticalFlow_Calibrator::sample_buffers_full() const
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{
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return ((_cal_data[0].num_samples >= ARRAY_SIZE(_cal_data[0].samples)) && (_cal_data[1].num_samples >= ARRAY_SIZE(_cal_data[1].samples)));
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}
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// run calibration algorithm for both axis
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// returns true on success and updates _cal_data[0,1].best_scale and best_scale_fitness
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bool AP_OpticalFlow_Calibrator::run_calibration()
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{
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// run calibration for x and y axis
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const bool calx_res = calc_scalars(0, _cal_data[0].best_scalar, _cal_data[0].best_scalar_fitness);
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const bool caly_res = calc_scalars(1, _cal_data[1].best_scalar, _cal_data[1].best_scalar_fitness);
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return calx_res && caly_res;
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}
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// Run Gauss Newton fitting algorithm for all samples of the given axis
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// returns a scalar and fitness (lower numbers mean a better result) in the arguments provided
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bool AP_OpticalFlow_Calibrator::calc_scalars(uint8_t axis, float& scalar, float& fitness)
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{
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// prefix for reporting
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const char* prefix_str = "FlowCal:";
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const char* axis_str = axis == 0 ? "x" : "y";
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// check we have samples
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// this should never fail because this method should only be called once the sample buffer is full
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const uint8_t num_samples = _cal_data[axis].num_samples;
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if (num_samples == 0) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s failed because no samples", prefix_str);
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return false;
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}
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// calculate total absolute residual from all samples
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float total_abs_residual = 0;
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for (uint8_t i = 0; i < num_samples; i++) {
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const sample_t& samplei = _cal_data[axis].samples[i];
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total_abs_residual += fabsf(calc_sample_residual(samplei, 1.0));
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}
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// if there are no residuals then scaling is perfect
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if (is_zero(total_abs_residual)) {
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scalar = 1.0;
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fitness = 0;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s perfect scalar%s of 1.0", prefix_str, axis_str);
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return true;
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}
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// for each sample calculate the residual and scalar that best reduces the residual
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float best_scalar_total = 0;
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for (uint8_t i = 0; i < num_samples; i++) {
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float sample_best_scalar;
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const sample_t& samplei = _cal_data[axis].samples[i];
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if (!calc_sample_best_scalar(samplei, sample_best_scalar)) {
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// failed to find the best scalar for a single sample
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// this should never happen because of checks when capturing samples
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s failed because of zero flow rate", prefix_str);
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INTERNAL_ERROR(AP_InternalError::error_t::flow_of_control);
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return false;
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}
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const float sample_residual = calc_sample_residual(samplei, 1.0);
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best_scalar_total += sample_best_scalar * fabsf(sample_residual) / total_abs_residual;
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}
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// check for out of range results
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if (best_scalar_total < AP_OPTICALFLOW_CAL_SCALE_MIN) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s scalar%s:%4.3f too low (<%3.1f)", prefix_str, axis_str, (double)best_scalar_total, (double)AP_OPTICALFLOW_CAL_SCALE_MIN);
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return false;
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}
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if (best_scalar_total > AP_OPTICALFLOW_CAL_SCALE_MAX) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s scalar%s:%4.3f too high (>%3.1f)", prefix_str, axis_str, (double)best_scalar_total, (double)AP_OPTICALFLOW_CAL_SCALE_MAX);
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return false;
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}
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// check for poor fitness
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float fitness_new = calc_mean_squared_residuals(axis, best_scalar_total);
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if (fitness_new > AP_OPTICALFLOW_CAL_FITNESS_THRESH) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s scalar%s:%4.3f fit:%4.3f too high (>%3.1f)", prefix_str, axis_str, (double)scalar, (double)fitness_new, (double)AP_OPTICALFLOW_CAL_FITNESS_THRESH);
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return false;
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}
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// success if fitness has improved
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float fitness_orig = calc_mean_squared_residuals(axis, 1.0);
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if (fitness_new <= fitness_orig) {
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scalar = best_scalar_total;
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fitness = fitness_new;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s scalar%s:%4.3f fit:%4.2f", prefix_str, axis_str, (double)scalar, (double)fitness);
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return true;
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}
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// failed to find a better scalar than 1.0
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scalar = 1.0;
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fitness = fitness_orig;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "%s no better scalar%s:%4.3f (fit:%4.3f > orig:%4.3f)", prefix_str, axis_str, (double)best_scalar_total, (double)fitness_new, (double)fitness_orig);
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return false;
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}
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// calculate a single sample's residual
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float AP_OpticalFlow_Calibrator::calc_sample_residual(const sample_t& sample, float scalar) const
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{
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return (sample.body_rate + ((sample.flow_rate * scalar) - sample.los_pred));
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}
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// calculate the scalar that minimises the residual for a single sample
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// returns true on success and populates the best_scalar argument
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bool AP_OpticalFlow_Calibrator::calc_sample_best_scalar(const sample_t& sample, float& best_scalar) const
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{
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// if sample's flow_rate is zero scalar has no effect
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// this should never happen because samples should have been checked before being added
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if (is_zero(sample.flow_rate)) {
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return false;
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}
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best_scalar = (sample.los_pred - sample.body_rate) / sample.flow_rate;
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return true;
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}
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// calculate mean squared residual for all samples of a single axis (0 or 1) given a scalar parameter
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float AP_OpticalFlow_Calibrator::calc_mean_squared_residuals(uint8_t axis, float scalar) const
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{
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// sanity check axis
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if (axis >= 2) {
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return AP_OPTICALFLOW_CAL_RMS_FAILED;
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}
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// calculate and sum residuals of each sample
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float sum = 0.0f;
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uint16_t num_samples = 0;
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for (uint8_t i = 0; i < _cal_data[axis].num_samples; i++) {
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sum += sq(calc_sample_residual(_cal_data[axis].samples[i], scalar));
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num_samples++;
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}
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// return a huge residual if no samples
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if (num_samples == 0) {
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return AP_OPTICALFLOW_CAL_RMS_FAILED;
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}
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sum /= num_samples;
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return sum;
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}
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// log all samples
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void AP_OpticalFlow_Calibrator::log_sample(uint8_t axis, uint8_t sample_num, float flow_rate, float body_rate, float los_pred)
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{
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// @LoggerMessage: OFCA
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// @Description: Optical Flow Calibration sample
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// @Field: TimeUS: Time since system startup
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// @Field: Axis: Axis (X=0 Y=1)
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// @Field: Num: Sample number
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// @Field: FRate: Flow rate
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// @Field: BRate: Body rate
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// @Field: LPred: Los pred
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AP::logger().Write("OFCA", "TimeUS,Axis,Num,FRate,BRate,LPred", "QBBfff",
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AP_HAL::micros64(),
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(unsigned)axis,
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(unsigned)sample_num,
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(double)flow_rate,
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(double)body_rate,
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(double)los_pred);
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}
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@ -0,0 +1,79 @@
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#pragma once
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#include <AP_HAL/AP_HAL.h>
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#include <AP_Math/AP_Math.h>
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#define AP_OPTICALFLOW_CAL_MAX_SAMPLES 50 // number of samples required before calibration begins
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class AP_OpticalFlow_Calibrator {
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public:
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AP_OpticalFlow_Calibrator() {};
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// start or stop the calibration
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void start();
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void stop();
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// update the state machine and calculate scaling
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// returns true if new scaling values have been found
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bool update();
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// get final scaling values
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// scaling values used during sample collection should be multiplied by these scalars
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Vector2f get_scalars();
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private:
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// single sample for a single axis
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struct sample_t {
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float flow_rate;
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float body_rate;
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float los_pred;
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};
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// attempt to add a new sample to the buffer
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void add_sample(uint32_t timestamp_ms, const Vector2f& flow_rate, const Vector2f& body_rate, const Vector2f& los_pred);
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// returns true once the sample buffer is full
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bool sample_buffers_full() const;
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// run calibration algorithm for both axis
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// returns true on success and updates _cal_data[0,1].best_scale and best_scale_fitness
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bool run_calibration();
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// Run fitting algorithm for all samples of the given axis
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// returns a scalar and fitness (lower numbers mean a better result) in the arguments provided
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bool calc_scalars(uint8_t axis, float& scalar, float& fitness);
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// calculate a single sample's residual given a scalar parameter
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float calc_sample_residual(const sample_t& sample, float scalar) const;
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// calculate the scalar that minimises the residual for a single sample
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// returns true on success and populates the best_scalar argument
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bool calc_sample_best_scalar(const sample_t& sample, float& best_scalar) const;
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// calculate mean squared residual for all samples of a single axis (0 or 1) given a scalar parameter
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float calc_mean_squared_residuals(uint8_t axis, float scalar) const;
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// log a sample
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void log_sample(uint8_t axis, uint8_t sample_num, float flow_rate, float body_rate, float los_pred);
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// calibration states
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enum class CalState {
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NOT_STARTED = 0,
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RUNNING, // collecting samples
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READY_TO_CALIBRATE, // ready to calibrate (may wait until vehicle is disarmed)
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SUCCESS,
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FAILED
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} _cal_state;
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// local variables
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uint32_t _start_time_ms; // time the calibration was started
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struct {
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sample_t samples[AP_OPTICALFLOW_CAL_MAX_SAMPLES]; // buffer of sensor samples
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uint8_t num_samples; // number of samples in samples buffer
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float best_scalar; // best scaling value found so far
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float best_scalar_fitness; // fitness (rms of error) of best scaling value
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} _cal_data[2]; // x and y axis
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uint32_t _last_sample_timestamp_ms; // system time of last sample's timestamp, used to ignore duplicates
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uint32_t _last_report_ms; // system time of last status report
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};
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