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ardupilot/libraries/AP_Compass/Compass_learn.cpp

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#include <AP_Math/AP_Math.h>
#include <AP_AHRS/AP_AHRS.h>
#include <AP_Compass/AP_Compass.h>
#include <AP_Declination/AP_Declination.h>
#include <AP_Logger/AP_Logger.h>
#include "Compass_learn.h"
#include <GCS_MAVLink/GCS.h>
#include <stdio.h>
#include <AP_Vehicle/AP_Vehicle.h>
#if COMPASS_LEARN_ENABLED
extern const AP_HAL::HAL &hal;
static const uint8_t COMPASS_LEARN_NUM_SAMPLES = 30;
static const uint8_t COMPASS_LEARN_BEST_ERROR_THRESHOLD = 50;
static const uint8_t COMPASS_LEARN_WORST_ERROR_THRESHOLD = 65;
// constructor
CompassLearn::CompassLearn(Compass &_compass) :
compass(_compass)
{
gcs().send_text(MAV_SEVERITY_INFO, "CompassLearn: Initialised");
for (Compass::Priority i(0); i<compass.get_count(); i++) {
if (compass._use_for_yaw[Compass::Priority(i)]) {
// reset scale factors, we can't learn scale factors in
// flight
compass.set_and_save_scale_factor(uint8_t(i), 0.0);
}
}
}
/*
update when new compass sample available
*/
void CompassLearn::update(void)
{
const AP_Vehicle *vehicle = AP::vehicle();
if (converged || compass.get_learn_type() != Compass::LEARN_INFLIGHT ||
!hal.util->get_soft_armed() || vehicle->get_time_flying_ms() < 3000) {
// only learn when flying and with enough time to be clear of
// the ground
return;
}
const AP_AHRS &ahrs = AP::ahrs();
if (!have_earth_field) {
Location loc;
if (!ahrs.get_position(loc)) {
// need to wait till we have a global position
return;
}
// setup the expected earth field in mGauss at this location
mag_ef = AP_Declination::get_earth_field_ga(loc) * 1000;
have_earth_field = true;
// form eliptical correction matrix and invert it. This is
// needed to remove the effects of the eliptical correction
// when calculating new offsets
const Vector3f &diagonals = compass.get_diagonals(0);
const Vector3f &offdiagonals = compass.get_offdiagonals(0);
mat = Matrix3f(
diagonals.x, offdiagonals.x, offdiagonals.y,
offdiagonals.x, diagonals.y, offdiagonals.z,
offdiagonals.y, offdiagonals.z, diagonals.z
);
if (!mat.invert()) {
// if we can't invert, use the identity matrix
mat.identity();
}
// set initial error to field intensity
float intensity = mag_ef.length();
for (uint16_t i=0; i<num_sectors; i++) {
errors[i] = intensity;
}
gcs().send_text(MAV_SEVERITY_INFO, "CompassLearn: have earth field");
hal.scheduler->register_io_process(FUNCTOR_BIND_MEMBER(&CompassLearn::io_timer, void));
}
AP_Notify::flags.compass_cal_running = true;
if (sample_available) {
// last sample still being processed by IO thread
return;
}
Vector3f field = compass.get_field(0);
Vector3f field_change = field - last_field;
if (field_change.length() < min_field_change) {
return;
}
{
WITH_SEMAPHORE(sem);
// give a sample to the backend to process
new_sample.field = field;
new_sample.offsets = compass.get_offsets(0);
new_sample.attitude = Vector3f(ahrs.roll, ahrs.pitch, ahrs.yaw);
sample_available = true;
last_field = field;
num_samples++;
}
if (sample_available) {
// @LoggerMessage: COFS
// @Description: Current compass learn offsets
// @Field: TimeUS: Time since system startup
// @Field: OfsX: best learnt offset, x-axis
// @Field: OfsY: best learnt offset, y-axis
// @Field: OfsZ: best learnt offset, z-axis
// @Field: Var: error of best offset vector
// @Field: Yaw: best learnt yaw
// @Field: WVar: error of best learn yaw
// @Field: N: number of samples used
AP::logger().Write("COFS", "TimeUS,OfsX,OfsY,OfsZ,Var,Yaw,WVar,N", "QffffffI",
AP_HAL::micros64(),
(double)best_offsets.x,
(double)best_offsets.y,
(double)best_offsets.z,
(double)best_error,
(double)best_yaw_deg,
(double)worst_error,
num_samples);
}
if (!converged) {
WITH_SEMAPHORE(sem);
// set offsets to current best guess
compass.set_offsets(0, best_offsets);
// set non-primary offsets to match primary
Vector3f field_primary = compass.get_field(0);
for (uint8_t i=1; i<compass.get_count(); i++) {
if (!compass._use_for_yaw[Compass::Priority(i)]) {
continue;
}
Vector3f field2 = compass.get_field(i);
Vector3f new_offsets = compass.get_offsets(i) + (field_primary - field2);
compass.set_offsets(i, new_offsets);
}
// stop updating the offsets once converged
if (num_samples > COMPASS_LEARN_NUM_SAMPLES &&
best_error < COMPASS_LEARN_BEST_ERROR_THRESHOLD &&
worst_error > COMPASS_LEARN_WORST_ERROR_THRESHOLD) {
// set the offsets and enable compass for EKF use. Let the
// EKF learn the remaining compass offset error
for (uint8_t i=0; i<compass.get_count(); i++) {
if (compass._use_for_yaw[Compass::Priority(i)]) {
compass.save_offsets(i);
compass.set_and_save_scale_factor(i, 0.0);
}
}
compass.set_learn_type(Compass::LEARN_NONE, true);
// setup so use can trigger it again
converged = false;
sample_available = false;
num_samples = 0;
have_earth_field = false;
for (auto &v : predicted_offsets) {
v.zero();
}
worst_error = 0;
best_error = 0;
best_yaw_deg = 0;
best_offsets.zero();
gcs().send_text(MAV_SEVERITY_INFO, "CompassLearn: finished");
AP_Notify::flags.compass_cal_running = false;
AP_Notify::events.compass_cal_saved = true;
}
}
}
/*
we run the math intensive calculations in the IO thread
*/
void CompassLearn::io_timer(void)
{
if (!sample_available) {
return;
}
struct sample s;
{
WITH_SEMAPHORE(sem);
s = new_sample;
sample_available = false;
}
process_sample(s);
}
/*
process a new compass sample
*/
void CompassLearn::process_sample(const struct sample &s)
{
uint16_t besti = 0;
float bestv = 0, worstv=0;
/*
we run through the 72 possible yaw error values, and for each
one we calculate a value for the compass offsets if that yaw
error is correct.
*/
for (uint16_t i=0; i<num_sectors; i++) {
float yaw_err_deg = i*(360/num_sectors);
// form rotation matrix for the euler attitude
Matrix3f dcm;
dcm.from_euler(s.attitude.x, s.attitude.y, wrap_2PI(s.attitude.z + radians(yaw_err_deg)));
// calculate the field we would expect to get if this yaw error is correct
Vector3f expected_field = dcm.transposed() * mag_ef;
// calculate a value for the compass offsets for this yaw error
Vector3f v1 = mat * s.field;
Vector3f v2 = mat * expected_field;
Vector3f offsets = (v2 - v1) + s.offsets;
float delta = (offsets - predicted_offsets[i]).length();
if (num_samples == 1) {
predicted_offsets[i] = offsets;
} else {
// lowpass the predicted offsets and the error
const float learn_rate = 0.92f;
predicted_offsets[i] = predicted_offsets[i] * learn_rate + offsets * (1-learn_rate);
errors[i] = errors[i] * learn_rate + delta * (1-learn_rate);
}
// keep track of the current best prediction
if (i == 0 || errors[i] < bestv) {
besti = i;
bestv = errors[i];
}
// also keep the worst error. This is used as part of the convergence test
if (i == 0 || errors[i] > worstv) {
worstv = errors[i];
}
}
WITH_SEMAPHORE(sem);
// pass the current estimate to the front-end
best_offsets = predicted_offsets[besti];
best_error = bestv;
worst_error = worstv;
best_yaw_deg = wrap_360(degrees(s.attitude.z) + besti * (360/num_sectors));
// send current learn state to gcs
const uint32_t now = AP_HAL::millis();
if (!converged && now - last_learn_progress_sent_ms >= 5000) {
float percent = (MIN(num_samples / COMPASS_LEARN_NUM_SAMPLES, 1.0f) +
MIN(COMPASS_LEARN_BEST_ERROR_THRESHOLD / (best_error + 1.0f), 1.0f) +
MIN(worst_error / COMPASS_LEARN_WORST_ERROR_THRESHOLD, 1.0f)) / 3.0f * 100.f;
gcs().send_text(MAV_SEVERITY_INFO, "CompassLearn: %d%%", (int) percent);
last_learn_progress_sent_ms = now;
}
}
#endif // COMPASS_LEARN_ENABLED
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