263 lines
8.4 KiB
C++
263 lines
8.4 KiB
C++
/*
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* @file AHRS.cpp
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* @brief Attitude and Heading Reference System implementation
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* Created on: Jan 26, 2012
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* Author: cbeall3
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*/
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#include "AHRS.h"
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#include <cmath>
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using namespace std;
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namespace gtsam {
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Matrix cov(const Matrix& m) {
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const double num_observations = m.cols();
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const Vector mean = m.rowwise().sum() / num_observations;
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Matrix D = m.colwise() - mean;
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Matrix DDt = D * trans(D);
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return DDt / (num_observations - 1);
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}
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Matrix I3 = eye(3);
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Matrix Z3 = zeros(3, 3);
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/* ************************************************************************* */
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AHRS::AHRS(const Matrix& stationaryU, const Matrix& stationaryF, double g_e,
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bool flat) :
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KF_(9) {
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// Initial state
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mech0_ = Mechanization_bRn2::initialize(stationaryU, stationaryF, g_e, flat);
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size_t T = stationaryU.cols();
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// estimate standard deviation on gyroscope readings
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Pg_ = cov(stationaryU);
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Vector sigmas_v_g = esqrt(Pg_.diagonal() * T);
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// estimate standard deviation on accelerometer readings
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Pa_ = cov(stationaryF);
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// Quantities needed for integration
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// dynamics, Chris' email September 23, 2011 3:38:05 PM EDT
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double tau_g = 730; // correlation time for gyroscope
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double tau_a = 730; // correlation time for accelerometer
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F_g_ = -I3 / tau_g;
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F_a_ = -I3 / tau_a;
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Vector var_omega_w = 0 * ones(3); // TODO
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Vector var_omega_g = (0.0034 * 0.0034) * ones(3);
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Vector var_omega_a = (0.034 * 0.034) * ones(3);
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Vector sigmas_v_g_sq = emul(sigmas_v_g, sigmas_v_g);
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Vector vars = concatVectors(4, &var_omega_w, &var_omega_g, &sigmas_v_g_sq,
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&var_omega_a);
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var_w_ = diag(vars);
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// Quantities needed for aiding
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sigmas_v_a_ = esqrt(T * Pa_.diagonal());
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// gravity in nav frame
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n_g_ = (Vector(3) << 0.0, 0.0, g_e);
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n_g_cross_ = skewSymmetric(n_g_); // nav frame has Z down !!!
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}
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/* ************************************************************************* */
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std::pair<Mechanization_bRn2, KalmanFilter::State> AHRS::initialize(double g_e) {
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// Calculate Omega_T, formula 2.80 in Farrell08book
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double cp = cos(mech0_.bRn().inverse().pitch());
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double sp = sin(mech0_.bRn().inverse().pitch());
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double cy = cos(0.0);
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double sy = sin(0.0);
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Matrix Omega_T = (Matrix(3, 3) << cy * cp, -sy, 0.0, sy * cp, cy, 0.0, -sp, 0.0, 1.0);
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// Calculate Jacobian of roll/pitch/yaw wrpt (g1,g2,g3), see doc/ypr.nb
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Vector b_g = mech0_.b_g(g_e);
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double g1 = b_g(0);
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double g2 = b_g(1);
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double g3 = b_g(2);
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double g23 = g2 * g2 + g3 * g3;
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double g123 = g1 * g1 + g23;
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double f = 1 / (std::sqrt(g23) * g123);
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Matrix H_g = (Matrix(3, 3) <<
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0.0, g3 / g23, -(g2 / g23), // roll
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std::sqrt(g23) / g123, -f * (g1 * g2), -f * (g1 * g3), // pitch
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0.0, 0.0, 0.0); // we don't know anything on yaw
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// Calculate the initial covariance matrix for the error state dx, Farrell08book eq. 10.66
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Matrix Pa = 0.025 * 0.025 * eye(3);
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Matrix P11 = Omega_T * (H_g * (Pa + Pa_) * trans(H_g)) * trans(Omega_T);
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P11(2, 2) = 0.0001;
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Matrix P12 = -Omega_T * H_g * Pa;
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Matrix P_plus_k2 = Matrix(9, 9);
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P_plus_k2.block<3,3>(0, 0) = P11;
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P_plus_k2.block<3,3>(0, 3) = Z3;
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P_plus_k2.block<3,3>(0, 6) = P12;
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P_plus_k2.block<3,3>(3, 0) = Z3;
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P_plus_k2.block<3,3>(3, 3) = Pg_;
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P_plus_k2.block<3,3>(3, 6) = Z3;
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P_plus_k2.block<3,3>(6, 0) = trans(P12);
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P_plus_k2.block<3,3>(6, 3) = Z3;
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P_plus_k2.block<3,3>(6, 6) = Pa;
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Vector dx = zero(9);
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KalmanFilter::State state = KF_.init(dx, P_plus_k2);
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return std::make_pair(mech0_, state);
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}
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/* ************************************************************************* */
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std::pair<Mechanization_bRn2, KalmanFilter::State> AHRS::integrate(
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const Mechanization_bRn2& mech, KalmanFilter::State state,
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const Vector& u, double dt) {
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// Integrate full state
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Mechanization_bRn2 newMech = mech.integrate(u, dt);
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// Integrate error state Kalman filter
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// FIXME:
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//if nargout>1
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Matrix bRn = mech.bRn().matrix();
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Matrix I3 = eye(3);
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Matrix Z3 = zeros(3, 3);
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Matrix F_k = zeros(9, 9);
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F_k.block<3,3>(0, 3) = -bRn;
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F_k.block<3,3>(3, 3) = F_g_;
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F_k.block<3,3>(6, 6) = F_a_;
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Matrix G_k = zeros(9, 12);
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G_k.block<3,3>(0, 0) = -bRn;
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G_k.block<3,3>(0, 6) = -bRn;
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G_k.block<3,3>(3, 3) = I3;
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G_k.block<3,3>(6, 9) = I3;
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Matrix Q_k = G_k * var_w_ * trans(G_k);
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Matrix Psi_k = eye(9) + dt * F_k; // +dt*dt*F_k*F_k/2; // transition matrix
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// This implements update in section 10.6
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Matrix B = zeros(9, 9);
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Vector u2 = zero(9);
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KalmanFilter::State newState = KF_.predictQ(state, Psi_k,B,u2,dt*Q_k);
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return make_pair(newMech, newState);
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}
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/* ************************************************************************* */
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bool AHRS::isAidingAvailable(const Mechanization_bRn2& mech,
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const gtsam::Vector& f, const gtsam::Vector& u, double ge) const {
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// Subtract the biases
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Vector f_ = f - mech.x_a();
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Vector u_ = u - mech.x_g();
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double mu_f = f_.norm() - ge; // accelerometer same magnitude as local gravity ?
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double mu_u = u_.norm(); // gyro says we are not maneuvering ?
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return (fabs(mu_f)<0.5 && mu_u < 5.0 / 180.0 * M_PI);
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}
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/* ************************************************************************* */
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std::pair<Mechanization_bRn2, KalmanFilter::State> AHRS::aid(
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const Mechanization_bRn2& mech, KalmanFilter::State state,
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const Vector& f, bool Farrell) const {
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Matrix bRn = mech.bRn().matrix();
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// get gravity in body from accelerometer
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Vector measured_b_g = mech.x_a() - f;
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Matrix R, H;
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Vector z;
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if (Farrell) {
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// calculate residual gravity measurement
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z = n_g_ - trans(bRn) * measured_b_g;
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H = collect(3, &n_g_cross_, &Z3, &bRn);
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R = trans(bRn) * diag(emul(sigmas_v_a_, sigmas_v_a_)) * bRn;
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} else {
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// my measurement prediction (in body frame):
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// F(:,k) = bias - b_g
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// F(:,k) = mech.x_a + dx_a - bRn*n_g;
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// F(:,k) = mech.x_a + dx_a - bRn*(I+P)*n_g;
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// F(:,k) = mech.x_a + dx_a - b_g - bRn*(rho x n_g); // P = [rho]_x
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// Hence, the measurement z = b_g - (mech.x_a - F(:,k)) is predicted
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// from the filter state (dx_a, rho) as dx_a + bRn*(n_g x rho)
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// z = b_g - (mech.x_a - F(:,k)) = dx_a + bRn*(n_g x rho)
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z = bRn * n_g_ - measured_b_g;
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// Now the Jacobian H
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Matrix b_g = bRn * n_g_cross_;
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H = collect(3, &b_g, &Z3, &I3);
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// And the measurement noise, TODO: should be created once where sigmas_v_a is given
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R = diag(emul(sigmas_v_a_, sigmas_v_a_));
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}
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// update the Kalman filter
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KalmanFilter::State updatedState = KF_.updateQ(state, H, z, R);
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// update full state (rotation and accelerometer bias)
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Mechanization_bRn2 newMech = mech.correct(updatedState->mean());
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// reset KF state
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Vector dx = zeros(9, 1);
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updatedState = KF_.init(dx, updatedState->covariance());
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return make_pair(newMech, updatedState);
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}
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/* ************************************************************************* */
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std::pair<Mechanization_bRn2, KalmanFilter::State> AHRS::aidGeneral(
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const Mechanization_bRn2& mech, KalmanFilter::State state,
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const Vector& f, const Vector& f_previous,
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const Rot3& R_previous) const {
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Matrix increment = R_previous.between(mech.bRn()).matrix();
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// expected - measured
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Vector z = f - increment * f_previous;
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//Vector z = increment * f_previous - f;
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Matrix b_g = skewSymmetric(increment* f_previous);
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Matrix H = collect(3, &b_g, &I3, &Z3);
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// Matrix R = diag(emul(sigmas_v_a_, sigmas_v_a_));
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// Matrix R = diag((Vector(3) << 1.0, 0.2, 1.0)); // good for L_twice
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Matrix R = diag((Vector(3) << 0.01, 0.0001, 0.01));
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// update the Kalman filter
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KalmanFilter::State updatedState = KF_.updateQ(state, H, z, R);
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// update full state (rotation and gyro bias)
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Mechanization_bRn2 newMech = mech.correct(updatedState->mean());
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// reset KF state
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Vector dx = zeros(9, 1);
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updatedState = KF_.init(dx, updatedState->covariance());
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return make_pair(newMech, updatedState);
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}
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/* ************************************************************************* */
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void AHRS::print(const std::string& s) const {
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mech0_.print(s + ".mech0_");
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gtsam::print(F_g_, s + ".F_g");
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gtsam::print(F_a_, s + ".F_a");
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gtsam::print(var_w_, s + ".var_w");
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gtsam::print(sigmas_v_a_, s + ".sigmas_v_a");
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gtsam::print(n_g_, s + ".n_g");
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gtsam::print(n_g_cross_, s + ".n_g_cross");
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gtsam::print(Pg_, s + ".P_g");
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gtsam::print(Pa_, s + ".P_a");
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}
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/* ************************************************************************* */
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AHRS::~AHRS() {
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}
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/* ************************************************************************* */
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} /* namespace gtsam */
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