mirror of
https://github.com/ArduPilot/ardupilot
synced 2025-01-29 20:18:31 -04:00
AP_Compass: new compass learning system
this learns compass offsets using magnetic tables and compass observations
This commit is contained in:
parent
6a89fdf268
commit
57a3bc1397
@ -1011,7 +1011,10 @@ Compass::use_for_yaw(void) const
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bool
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Compass::use_for_yaw(uint8_t i) const
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{
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return _state[i].use_for_yaw;
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// when we are doing in-flight compass learning the state
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// estimator must not use the compass. The learning code turns off
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// inflight learning when it has converged
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return _state[i].use_for_yaw && _learn.get() != LEARN_INFLIGHT;
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}
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void
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@ -52,6 +52,8 @@ public:
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Compass(const Compass &other) = delete;
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Compass &operator=(const Compass&) = delete;
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friend class CompassLearn;
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/// Initialize the compass device.
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///
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/// @returns True if the compass was initialized OK, false if it was not
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@ -182,14 +184,14 @@ public:
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// learn offsets accessor
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bool learn_offsets_enabled() const { return _learn; }
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/// Perform automatic offset updates
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///
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void learn_offsets(void);
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/// return true if the compass should be used for yaw calculations
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bool use_for_yaw(uint8_t i) const;
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bool use_for_yaw(void) const;
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void set_use_for_yaw(uint8_t i, bool use) {
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_state[i].use_for_yaw.set(use);
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}
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/// Sets the local magnetic field declination.
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///
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/// @param radians Local field declination.
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@ -301,7 +303,8 @@ public:
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enum LearnType {
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LEARN_NONE=0,
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LEARN_INTERNAL=1,
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LEARN_EKF=2
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LEARN_EKF=2,
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LEARN_INFLIGHT=3
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};
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// return the chosen learning type
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@ -309,6 +312,15 @@ public:
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return (enum LearnType)_learn.get();
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}
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// set the learning type
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void set_learn_type(enum LearnType type, bool save) {
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if (save) {
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_learn.set_and_save((int8_t)type);
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} else {
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_learn.set((int8_t)type);
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}
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}
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// return maximum allowed compass offsets
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uint16_t get_offsets_max(void) const {
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return (uint16_t)_offset_max.get();
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@ -1,129 +1,203 @@
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#include <AP_Math/AP_Math.h>
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#include <AP_AHRS/AP_AHRS.h>
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#include "AP_Compass.h"
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#include <AP_Compass/AP_Compass.h>
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#include <AP_Declination/AP_Declination.h>
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#include <DataFlash/DataFlash.h>
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// don't allow any axis of the offset to go above 2000
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#define COMPASS_OFS_LIMIT 2000
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#include "Compass_learn.h"
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#include <stdio.h>
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extern const AP_HAL::HAL &hal;
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// constructor
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CompassLearn::CompassLearn(AP_AHRS &_ahrs, Compass &_compass) :
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ahrs(_ahrs),
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compass(_compass)
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{
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}
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/*
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* this offset learning algorithm is inspired by this paper from Bill Premerlani
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*
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* http://gentlenav.googlecode.com/files/MagnetometerOffsetNullingRevisited.pdf
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*
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* The base algorithm works well, but is quite sensitive to
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* noise. After long discussions with Bill, the following changes were
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* made:
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*
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* 1) we keep a history buffer that effectively divides the mag
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* vectors into a set of N streams. The algorithm is run on the
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* streams separately
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*
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* 2) within each stream we only calculate a change when the mag
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* vector has changed by a significant amount.
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*
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* This gives us the property that we learn quickly if there is no
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* noise, but still learn correctly (and slowly) in the face of lots of
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* noise.
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update when new compass sample available
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*/
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void
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Compass::learn_offsets(void)
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void CompassLearn::update(void)
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{
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if (_learn == 0) {
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// auto-calibration is disabled
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if (converged || compass.get_learn_type() != Compass::LEARN_INFLIGHT ||
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!hal.util->get_soft_armed() || ahrs.get_time_flying_ms() < 3000) {
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// only learn when flying and with enough time to be clear of
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// the ground
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return;
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}
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// this gain is set so we converge on the offsets in about 5
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// minutes with a 10Hz compass
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const float gain = 0.01f;
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const float max_change = 10.0f;
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const float min_diff = 50.0f;
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if (!_null_init_done) {
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// first time through
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_null_init_done = true;
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for (uint8_t k=0; k<COMPASS_MAX_INSTANCES; k++) {
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const Vector3f &field = _state[k].field;
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const Vector3f &ofs = _state[k].offset.get();
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for (uint8_t i=0; i<_mag_history_size; i++) {
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// fill the history buffer with the current mag vector,
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// with the offset removed
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_state[k].mag_history[i] = Vector3i(roundf(field.x) - ofs.x,
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roundf(field.y) - ofs.y,
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roundf(field.z) - ofs.z);
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}
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_state[k].mag_history_index = 0;
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if (!have_earth_field) {
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Location loc;
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if (!ahrs.get_position(loc)) {
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// need to wait till we have a global position
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return;
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}
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// setup the expected earth field at this location
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float declination_deg=0, inclination_deg=0, intensity_gauss=0;
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AP_Declination::get_mag_field_ef(loc.lat*1.0e-7, loc.lng*1.0e-7, intensity_gauss, declination_deg, inclination_deg);
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// create earth field
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mag_ef = Vector3f(intensity_gauss*1000, 0.0, 0.0);
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Matrix3f R;
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R.from_euler(0.0f, -ToRad(inclination_deg), ToRad(declination_deg));
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mag_ef = R * mag_ef;
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sem = hal.util->new_semaphore();
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have_earth_field = true;
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// form eliptical correction matrix and invert it. This is
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// needed to remove the effects of the eliptical correction
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// when calculating new offsets
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const Vector3f &diagonals = compass.get_diagonals(0);
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const Vector3f &offdiagonals = compass.get_offdiagonals(0);
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mat = Matrix3f(
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diagonals.x, offdiagonals.x, offdiagonals.y,
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offdiagonals.x, diagonals.y, offdiagonals.z,
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offdiagonals.y, offdiagonals.z, diagonals.z
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);
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if (!mat.invert()) {
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// if we can't invert, use the identity matrix
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mat.identity();
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}
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// set initial error to field intensity
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for (uint16_t i=0; i<num_sectors; i++) {
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errors[i] = intensity_gauss*1000;
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}
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hal.scheduler->register_io_process(FUNCTOR_BIND_MEMBER(&CompassLearn::io_timer, void));
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}
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if (sample_available) {
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// last sample still being processed by IO thread
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return;
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}
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for (uint8_t k=0; k<COMPASS_MAX_INSTANCES; k++) {
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const Vector3f &ofs = _state[k].offset.get();
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const Vector3f &field = _state[k].field;
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Vector3f b1, diff;
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float length;
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Vector3f field = compass.get_field(0);
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Vector3f field_change = field - last_field;
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if (field_change.length() < min_field_change) {
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return;
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}
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if (sem->take_nonblocking()) {
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// give a sample to the backend to process
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new_sample.field = field;
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new_sample.offsets = compass.get_offsets(0);
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new_sample.attitude = Vector3f(ahrs.roll, ahrs.pitch, ahrs.yaw);
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sample_available = true;
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last_field = field;
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num_samples++;
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sem->give();
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}
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if (ofs.is_nan()) {
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// offsets are bad possibly due to a past bug - zero them
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_state[k].offset.set(Vector3f());
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if (sample_available) {
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DataFlash_Class::instance()->Log_Write("COFS", "TimeUS,OfsX,OfsY,OfsZ,Var,Yaw,WVar,N", "QffffffI",
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AP_HAL::micros64(),
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best_offsets.x,
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best_offsets.y,
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best_offsets.z,
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best_error,
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best_yaw_deg,
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worst_error,
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num_samples);
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}
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if (!converged && sem->take_nonblocking()) {
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// stop updating the offsets once converged
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compass.set_offsets(0, best_offsets);
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if (num_samples > 30 && best_error < 50 && worst_error > 65) {
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// set the offsets and enable compass for EKF use. Let the
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// EKF learn the remaining compass offset error
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compass.save_offsets(0);
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compass.set_use_for_yaw(0, true);
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compass.set_learn_type(Compass::LEARN_EKF, true);
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converged = true;
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}
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// get a past element
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b1 = Vector3f(_state[k].mag_history[_state[k].mag_history_index].x,
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_state[k].mag_history[_state[k].mag_history_index].y,
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_state[k].mag_history[_state[k].mag_history_index].z);
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// the history buffer doesn't have the offsets
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b1 += ofs;
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// get the current vector
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const Vector3f &b2 = field;
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// calculate the delta for this sample
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diff = b2 - b1;
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length = diff.length();
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if (length < min_diff) {
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// the mag vector hasn't changed enough - we don't get
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// enough information from this vector to use it.
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// Note that we don't put the current vector into the mag
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// history here. We want to wait for a larger rotation to
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// build up before calculating an offset change, as accuracy
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// of the offset change is highly dependent on the size of the
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// rotation.
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_state[k].mag_history_index = (_state[k].mag_history_index + 1) % _mag_history_size;
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continue;
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}
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// put the vector in the history
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_state[k].mag_history[_state[k].mag_history_index] = Vector3i(roundf(field.x) - ofs.x,
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roundf(field.y) - ofs.y,
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roundf(field.z) - ofs.z);
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_state[k].mag_history_index = (_state[k].mag_history_index + 1) % _mag_history_size;
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// equation 6 of Bills paper
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diff = diff * (gain * (b2.length() - b1.length()) / length);
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// limit the change from any one reading. This is to prevent
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// single crazy readings from throwing off the offsets for a long
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// time
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length = diff.length();
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if (length > max_change) {
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diff *= max_change / length;
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}
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Vector3f new_offsets = _state[k].offset.get() - diff;
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if (new_offsets.is_nan()) {
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// don't apply bad offsets
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continue;
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}
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// constrain offsets
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new_offsets.x = constrain_float(new_offsets.x, -COMPASS_OFS_LIMIT, COMPASS_OFS_LIMIT);
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new_offsets.y = constrain_float(new_offsets.y, -COMPASS_OFS_LIMIT, COMPASS_OFS_LIMIT);
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new_offsets.z = constrain_float(new_offsets.z, -COMPASS_OFS_LIMIT, COMPASS_OFS_LIMIT);
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// set the new offsets
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_state[k].offset.set(new_offsets);
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sem->give();
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}
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}
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/*
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we run the math intensive calculations in the IO thread
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*/
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void CompassLearn::io_timer(void)
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{
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if (!sample_available) {
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return;
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}
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struct sample s;
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if (!sem->take_nonblocking()) {
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return;
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}
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s = new_sample;
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sample_available = false;
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sem->give();
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process_sample(s);
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}
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/*
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process a new compass sample
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*/
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void CompassLearn::process_sample(const struct sample &s)
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{
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uint16_t besti = 0;
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float bestv = 0, worstv=0;
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/*
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we run through the 72 possible yaw error values, and for each
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one we calculate a value for the compass offsets if that yaw
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error is correct.
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*/
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for (uint16_t i=0; i<num_sectors; i++) {
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float yaw_err_deg = i*(360/num_sectors);
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// form rotation matrix for the euler attitude
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Matrix3f dcm;
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dcm.from_euler(s.attitude.x, s.attitude.y, wrap_2PI(s.attitude.z + radians(yaw_err_deg)));
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// calculate the field we would expect to get if this yaw error is correct
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Vector3f expected_field = dcm.transposed() * mag_ef;
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// calculate a value for the compass offsets for this yaw error
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Vector3f v1 = mat * s.field;
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Vector3f v2 = mat * expected_field;
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Vector3f offsets = (v2 - v1) + s.offsets;
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float delta = (offsets - predicted_offsets[i]).length();
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if (num_samples == 1) {
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predicted_offsets[i] = offsets;
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} else {
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// lowpass the predicted offsets and the error
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const float learn_rate = 0.92;
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predicted_offsets[i] = predicted_offsets[i] * learn_rate + offsets * (1-learn_rate);
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errors[i] = errors[i] * learn_rate + delta * (1-learn_rate);
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}
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// keep track of the current best prediction
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if (i == 0 || errors[i] < bestv) {
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besti = i;
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bestv = errors[i];
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}
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// also keep the worst error. This is used as part of the convergence test
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if (i == 0 || errors[i] > worstv) {
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worstv = errors[i];
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}
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}
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if (sem->take_nonblocking()) {
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// pass the current estimate to the front-end
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best_offsets = predicted_offsets[besti];
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best_error = bestv;
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worst_error = worstv;
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best_yaw_deg = wrap_360(degrees(s.attitude.z) + besti * (360/num_sectors));
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sem->give();
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}
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}
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57
libraries/AP_Compass/Compass_learn.h
Normal file
57
libraries/AP_Compass/Compass_learn.h
Normal file
@ -0,0 +1,57 @@
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#pragma once
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/*
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compass learning using magnetic field tables from AP_Declination
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*/
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class CompassLearn {
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public:
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CompassLearn(AP_AHRS &ahrs, Compass &compass);
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// called on each compass read
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void update(void);
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private:
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// reference to AHRS library. Needed for attitude, position and compass
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const AP_AHRS &ahrs;
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Compass &compass;
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bool have_earth_field;
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// 5 degree resolution
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static const uint16_t num_sectors = 72;
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Vector3f predicted_offsets[num_sectors];
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float errors[num_sectors];
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uint32_t num_samples;
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// earth field
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Vector3f mag_ef;
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// semaphore for access to shared data with IO thread
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AP_HAL::Semaphore *sem;
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struct sample {
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// milliGauss body field and offsets
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Vector3f field;
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Vector3f offsets;
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// euler radians attitude
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Vector3f attitude;
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};
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Matrix3f mat;
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struct sample new_sample;
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bool sample_available;
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Vector3f last_field;
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static const uint32_t min_field_change = 60;
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Vector3f best_offsets;
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float best_error;
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float best_yaw_deg;
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float worst_error;
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bool converged;
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void io_timer(void);
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void process_sample(const struct sample &s);
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};
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@ -62,7 +62,6 @@ static void loop()
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// use roll = 0, pitch = 0 for this example
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dcm_matrix.from_euler(0, 0, 0);
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heading = compass.calculate_heading(dcm_matrix, i);
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compass.learn_offsets();
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const Vector3f &mag = compass.get_field(i);
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