forked from Archive/PX4-Autopilot
418 lines
13 KiB
C++
418 lines
13 KiB
C++
/****************************************************************************
|
|
*
|
|
* Copyright (c) 2018 Estimation and Control Library (ECL). All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
*
|
|
* 1. Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* 2. Redistributions in binary form must reproduce the above copyright
|
|
* notice, this list of conditions and the following disclaimer in
|
|
* the documentation and/or other materials provided with the
|
|
* distribution.
|
|
* 3. Neither the name ECL nor the names of its contributors may be
|
|
* used to endorse or promote products derived from this software
|
|
* without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
|
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
|
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
|
|
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
|
|
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
|
|
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
|
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
|
|
* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
|
|
* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
|
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
|
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
* POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
****************************************************************************/
|
|
|
|
/**
|
|
* @file gps_yaw_fusion.cpp
|
|
* Definition of functions required to use yaw obtained from GPS dual antenna measurements.
|
|
*
|
|
* @author Paul Riseborough <p_riseborough@live.com.au>
|
|
*
|
|
*/
|
|
|
|
#include "ekf.h"
|
|
|
|
#include <ecl.h>
|
|
#include <mathlib/mathlib.h>
|
|
#include <cstdlib>
|
|
|
|
void Ekf::fuseGpsAntYaw()
|
|
{
|
|
// assign intermediate state variables
|
|
const float q0 = _state.quat_nominal(0);
|
|
const float q1 = _state.quat_nominal(1);
|
|
const float q2 = _state.quat_nominal(2);
|
|
const float q3 = _state.quat_nominal(3);
|
|
|
|
float R_YAW = 1.0f;
|
|
float predicted_hdg;
|
|
float H_YAW[4];
|
|
float measured_hdg;
|
|
|
|
// check if data has been set to NAN indicating no measurement
|
|
if (ISFINITE(_gps_sample_delayed.yaw)) {
|
|
// calculate the observed yaw angle of antenna array, converting a from body to antenna yaw measurement
|
|
measured_hdg = _gps_sample_delayed.yaw + _gps_yaw_offset;
|
|
|
|
// define the predicted antenna array vector and rotate into earth frame
|
|
Vector3f ant_vec_bf = {cosf(_gps_yaw_offset), sinf(_gps_yaw_offset), 0.0f};
|
|
Vector3f ant_vec_ef = _R_to_earth * ant_vec_bf;
|
|
|
|
// check if antenna array vector is within 30 degrees of vertical and therefore unable to provide a reliable heading
|
|
if (fabsf(ant_vec_ef(2)) > cosf(math::radians(30.0f))) {
|
|
return;
|
|
}
|
|
|
|
// calculate predicted antenna yaw angle
|
|
predicted_hdg = atan2f(ant_vec_ef(1),ant_vec_ef(0));
|
|
|
|
// calculate observation jacobian
|
|
float t2 = sinf(_gps_yaw_offset);
|
|
float t3 = cosf(_gps_yaw_offset);
|
|
float t4 = q0*q3*2.0f;
|
|
float t5 = q0*q0;
|
|
float t6 = q1*q1;
|
|
float t7 = q2*q2;
|
|
float t8 = q3*q3;
|
|
float t9 = q1*q2*2.0f;
|
|
float t10 = t5+t6-t7-t8;
|
|
float t11 = t3*t10;
|
|
float t12 = t4+t9;
|
|
float t13 = t3*t12;
|
|
float t14 = t5-t6+t7-t8;
|
|
float t15 = t2*t14;
|
|
float t16 = t13+t15;
|
|
float t17 = t4-t9;
|
|
float t19 = t2*t17;
|
|
float t20 = t11-t19;
|
|
float t18 = (t20*t20);
|
|
if (t18 < 1e-6f) {
|
|
return;
|
|
}
|
|
t18 = 1.0f / t18;
|
|
float t21 = t16*t16;
|
|
float t22 = sq(t11-t19);
|
|
if (t22 < 1e-6f) {
|
|
return;
|
|
}
|
|
t22 = 1.0f/t22;
|
|
float t23 = q1*t3*2.0f;
|
|
float t24 = q2*t2*2.0f;
|
|
float t25 = t23+t24;
|
|
float t26 = 1.0f/t20;
|
|
float t27 = q1*t2*2.0f;
|
|
float t28 = t21*t22;
|
|
float t29 = t28+1.0f;
|
|
if (fabsf(t29) < 1e-6f) {
|
|
return;
|
|
}
|
|
float t30 = 1.0f/t29;
|
|
float t31 = q0*t3*2.0f;
|
|
float t32 = t31-q3*t2*2.0f;
|
|
float t33 = q3*t3*2.0f;
|
|
float t34 = q0*t2*2.0f;
|
|
float t35 = t33+t34;
|
|
|
|
H_YAW[0] = (t35/(t11-t2*(t4-q1*q2*2.0f))-t16*t18*t32)/(t18*t21+1.0f);
|
|
H_YAW[1] = -t30*(t26*(t27-q2*t3*2.0f)+t16*t22*t25);
|
|
H_YAW[2] = t30*(t25*t26-t16*t22*(t27-q2*t3*2.0f));
|
|
H_YAW[3] = t30*(t26*t32+t16*t22*t35);
|
|
|
|
// using magnetic heading tuning parameter
|
|
R_YAW = sq(fmaxf(_params.mag_heading_noise, 1.0e-2f));
|
|
|
|
} else {
|
|
// there is nothing to fuse
|
|
return;
|
|
}
|
|
|
|
// wrap the heading to the interval between +-pi
|
|
measured_hdg = wrap_pi(measured_hdg);
|
|
|
|
// calculate the innovation and define the innovation gate
|
|
float innov_gate = math::max(_params.heading_innov_gate, 1.0f);
|
|
_heading_innov = predicted_hdg - measured_hdg;
|
|
|
|
// wrap the innovation to the interval between +-pi
|
|
_heading_innov = wrap_pi(_heading_innov);
|
|
|
|
// Calculate innovation variance and Kalman gains, taking advantage of the fact that only the first 3 elements in H are non zero
|
|
// calculate the innovation variance
|
|
float PH[4];
|
|
_heading_innov_var = R_YAW;
|
|
|
|
for (unsigned row = 0; row <= 3; row++) {
|
|
PH[row] = 0.0f;
|
|
|
|
for (uint8_t col = 0; col <= 3; col++) {
|
|
PH[row] += P(row,col) * H_YAW[col];
|
|
}
|
|
|
|
_heading_innov_var += H_YAW[row] * PH[row];
|
|
}
|
|
|
|
float heading_innov_var_inv;
|
|
|
|
// check if the innovation variance calculation is badly conditioned
|
|
if (_heading_innov_var >= R_YAW) {
|
|
// the innovation variance contribution from the state covariances is not negative, no fault
|
|
_fault_status.flags.bad_hdg = false;
|
|
heading_innov_var_inv = 1.0f / _heading_innov_var;
|
|
|
|
} else {
|
|
// the innovation variance contribution from the state covariances is negative which means the covariance matrix is badly conditioned
|
|
_fault_status.flags.bad_hdg = true;
|
|
|
|
// we reinitialise the covariance matrix and abort this fusion step
|
|
initialiseCovariance();
|
|
ECL_ERR_TIMESTAMPED("GPS yaw fusion numerical error - covariance reset");
|
|
return;
|
|
}
|
|
|
|
// calculate the Kalman gains
|
|
// only calculate gains for states we are using
|
|
float Kfusion[_k_num_states] = {};
|
|
|
|
for (uint8_t row = 0; row <= 15; row++) {
|
|
Kfusion[row] = 0.0f;
|
|
|
|
for (uint8_t col = 0; col <= 3; col++) {
|
|
Kfusion[row] += P(row,col) * H_YAW[col];
|
|
}
|
|
|
|
Kfusion[row] *= heading_innov_var_inv;
|
|
}
|
|
|
|
if (_control_status.flags.wind) {
|
|
for (uint8_t row = 22; row <= 23; row++) {
|
|
Kfusion[row] = 0.0f;
|
|
|
|
for (uint8_t col = 0; col <= 3; col++) {
|
|
Kfusion[row] += P(row,col) * H_YAW[col];
|
|
}
|
|
|
|
Kfusion[row] *= heading_innov_var_inv;
|
|
}
|
|
}
|
|
|
|
// innovation test ratio
|
|
_yaw_test_ratio = sq(_heading_innov) / (sq(innov_gate) * _heading_innov_var);
|
|
|
|
// we are no longer using 3-axis fusion so set the reported test levels to zero
|
|
memset(_mag_test_ratio, 0, sizeof(_mag_test_ratio));
|
|
|
|
// set the magnetometer unhealthy if the test fails
|
|
if (_yaw_test_ratio > 1.0f) {
|
|
_innov_check_fail_status.flags.reject_yaw = true;
|
|
|
|
// if we are in air we don't want to fuse the measurement
|
|
// we allow to use it when on the ground because the large innovation could be caused
|
|
// by interference or a large initial gyro bias
|
|
if (_control_status.flags.in_air) {
|
|
return;
|
|
|
|
} else {
|
|
// constrain the innovation to the maximum set by the gate
|
|
float gate_limit = sqrtf((sq(innov_gate) * _heading_innov_var));
|
|
_heading_innov = math::constrain(_heading_innov, -gate_limit, gate_limit);
|
|
}
|
|
|
|
} else {
|
|
_innov_check_fail_status.flags.reject_yaw = false;
|
|
}
|
|
|
|
// apply covariance correction via P_new = (I -K*H)*P
|
|
// first calculate expression for KHP
|
|
// then calculate P - KHP
|
|
matrix::SquareMatrix<float, _k_num_states> KHP;
|
|
float KH[4];
|
|
|
|
for (unsigned row = 0; row < _k_num_states; row++) {
|
|
|
|
KH[0] = Kfusion[row] * H_YAW[0];
|
|
KH[1] = Kfusion[row] * H_YAW[1];
|
|
KH[2] = Kfusion[row] * H_YAW[2];
|
|
KH[3] = Kfusion[row] * H_YAW[3];
|
|
|
|
for (unsigned column = 0; column < _k_num_states; column++) {
|
|
float tmp = KH[0] * P(0,column);
|
|
tmp += KH[1] * P(1,column);
|
|
tmp += KH[2] * P(2,column);
|
|
tmp += KH[3] * P(3,column);
|
|
KHP(row,column) = tmp;
|
|
}
|
|
}
|
|
|
|
// if the covariance correction will result in a negative variance, then
|
|
// the covariance matrix is unhealthy and must be corrected
|
|
bool healthy = true;
|
|
_fault_status.flags.bad_hdg = false;
|
|
|
|
for (int i = 0; i < _k_num_states; i++) {
|
|
if (P(i,i) < KHP(i,i)) {
|
|
// zero rows and columns
|
|
P.uncorrelateCovarianceSetVariance<1>(i, 0.0f);
|
|
|
|
//flag as unhealthy
|
|
healthy = false;
|
|
|
|
// update individual measurement health status
|
|
_fault_status.flags.bad_hdg = true;
|
|
|
|
}
|
|
}
|
|
|
|
// only apply covariance and state corrections if healthy
|
|
if (healthy) {
|
|
// apply the covariance corrections
|
|
for (unsigned row = 0; row < _k_num_states; row++) {
|
|
for (unsigned column = 0; column < _k_num_states; column++) {
|
|
P(row,column) = P(row,column) - KHP(row,column);
|
|
}
|
|
}
|
|
|
|
// correct the covariance matrix for gross errors
|
|
fixCovarianceErrors(true);
|
|
|
|
// apply the state corrections
|
|
fuse(Kfusion, _heading_innov);
|
|
|
|
}
|
|
}
|
|
|
|
bool Ekf::resetGpsAntYaw()
|
|
{
|
|
// check if data has been set to NAN indicating no measurement
|
|
if (ISFINITE(_gps_sample_delayed.yaw)) {
|
|
|
|
// define the predicted antenna array vector and rotate into earth frame
|
|
const Vector3f ant_vec_bf = {cosf(_gps_yaw_offset), sinf(_gps_yaw_offset), 0.0f};
|
|
const Vector3f ant_vec_ef = _R_to_earth * ant_vec_bf;
|
|
|
|
// check if antenna array vector is within 30 degrees of vertical and therefore unable to provide a reliable heading
|
|
if (fabsf(ant_vec_ef(2)) > cosf(math::radians(30.0f))) {
|
|
return false;
|
|
}
|
|
|
|
const float predicted_yaw = atan2f(ant_vec_ef(1),ant_vec_ef(0));
|
|
|
|
// get measurement and correct for antenna array yaw offset
|
|
const float measured_yaw = _gps_sample_delayed.yaw + _gps_yaw_offset;
|
|
|
|
// calculate the amount the yaw needs to be rotated by
|
|
float yaw_delta = wrap_pi(measured_yaw - predicted_yaw);
|
|
|
|
// save a copy of the quaternion state for later use in calculating the amount of reset change
|
|
const Quatf quat_before_reset = _state.quat_nominal;
|
|
Quatf quat_after_reset = _state.quat_nominal;
|
|
|
|
// obtain the yaw angle using the best conditioned from either a Tait-Bryan 321 or 312 sequence
|
|
// to avoid gimbal lock
|
|
if (fabsf(_R_to_earth(2, 0)) < fabsf(_R_to_earth(2, 1))) {
|
|
// get the roll, pitch, yaw estimates from the quaternion states using a 321 Tait-Bryan rotation sequence
|
|
Eulerf euler_init(_state.quat_nominal);
|
|
|
|
// correct the yaw angle
|
|
euler_init(2) += yaw_delta;
|
|
euler_init(2) = wrap_pi(euler_init(2));
|
|
|
|
quat_after_reset = Quatf(euler_init);
|
|
|
|
} else {
|
|
// Calculate the 312 Tait-Bryan sequence euler angles that rotate from earth to body frame
|
|
// PX4 math library does not support this so are using equations from
|
|
// http://www.atacolorado.com/eulersequences.doc
|
|
Vector3f euler312;
|
|
euler312(0) = atan2f(-_R_to_earth(0, 1), _R_to_earth(1, 1)); // first rotation (yaw)
|
|
euler312(1) = asinf(_R_to_earth(2, 1)); // second rotation (roll)
|
|
euler312(2) = atan2f(-_R_to_earth(2, 0), _R_to_earth(2, 2)); // third rotation (pitch)
|
|
|
|
// correct the yaw angle
|
|
euler312(0) += yaw_delta;
|
|
euler312(0) = wrap_pi(euler312(0));
|
|
|
|
// Calculate the body to earth frame rotation matrix from the corrected euler angles
|
|
float c2 = cosf(euler312(2));
|
|
float s2 = sinf(euler312(2));
|
|
float s1 = sinf(euler312(1));
|
|
float c1 = cosf(euler312(1));
|
|
float s0 = sinf(euler312(0));
|
|
float c0 = cosf(euler312(0));
|
|
|
|
Dcmf R_to_earth;
|
|
R_to_earth(0, 0) = c0 * c2 - s0 * s1 * s2;
|
|
R_to_earth(1, 1) = c0 * c1;
|
|
R_to_earth(2, 2) = c2 * c1;
|
|
R_to_earth(0, 1) = -c1 * s0;
|
|
R_to_earth(0, 2) = s2 * c0 + c2 * s1 * s0;
|
|
R_to_earth(1, 0) = c2 * s0 + s2 * s1 * c0;
|
|
R_to_earth(1, 2) = s0 * s2 - s1 * c0 * c2;
|
|
R_to_earth(2, 0) = -s2 * c1;
|
|
R_to_earth(2, 1) = s1;
|
|
|
|
// update the quaternions
|
|
quat_after_reset = Quatf(R_to_earth);
|
|
}
|
|
|
|
// calculate the amount that the quaternion has changed by
|
|
const Quatf q_error( (_state.quat_nominal * quat_before_reset.inversed()).normalized() );
|
|
|
|
// convert the quaternion delta to a delta angle
|
|
Vector3f delta_ang_error;
|
|
float scalar;
|
|
|
|
if (q_error(0) >= 0.0f) {
|
|
scalar = -2.0f;
|
|
|
|
} else {
|
|
scalar = 2.0f;
|
|
}
|
|
|
|
delta_ang_error(0) = scalar * q_error(1);
|
|
delta_ang_error(1) = scalar * q_error(2);
|
|
delta_ang_error(2) = scalar * q_error(3);
|
|
|
|
// update the quaternion state estimates and corresponding covariances only if the change in angle has been large or the yaw is not yet aligned
|
|
if (delta_ang_error.norm() > math::radians(15.0f) || !_control_status.flags.yaw_align) {
|
|
// update quaternion states
|
|
_state.quat_nominal = quat_after_reset;
|
|
uncorrelateQuatStates();
|
|
|
|
// record the state change
|
|
_state_reset_status.quat_change = q_error;
|
|
|
|
// update transformation matrix from body to world frame using the current estimate
|
|
_R_to_earth = Dcmf(_state.quat_nominal);
|
|
|
|
// update the yaw angle variance using the variance of the measurement
|
|
increaseQuatYawErrVariance(sq(fmaxf(_params.mag_heading_noise, 1.0e-2f)));
|
|
|
|
// add the reset amount to the output observer buffered data
|
|
for (uint8_t i = 0; i < _output_buffer.get_length(); i++) {
|
|
_output_buffer[i].quat_nominal = _state_reset_status.quat_change * _output_buffer[i].quat_nominal;
|
|
}
|
|
|
|
// apply the change in attitude quaternion to our newest quaternion estimate
|
|
// which was already taken out from the output buffer
|
|
_output_new.quat_nominal = _state_reset_status.quat_change * _output_new.quat_nominal;
|
|
|
|
// capture the reset event
|
|
_state_reset_status.quat_counter++;
|
|
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|