178 lines
8.2 KiB
C++
178 lines
8.2 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file ImuFactor.cpp
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* @author Luca Carlone
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* @author Stephen Williams
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* @author Richard Roberts
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* @author Vadim Indelman
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* @author David Jensen
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* @author Frank Dellaert
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**/
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#include <gtsam/navigation/ImuFactor.h>
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/* External or standard includes */
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#include <ostream>
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namespace gtsam {
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using namespace std;
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//------------------------------------------------------------------------------
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// Inner class PreintegratedMeasurements
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//------------------------------------------------------------------------------
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ImuFactor::PreintegratedMeasurements::PreintegratedMeasurements(
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const imuBias::ConstantBias& bias, const Matrix3& measuredAccCovariance,
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const Matrix3& measuredOmegaCovariance, const Matrix3& integrationErrorCovariance,
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const bool use2ndOrderIntegration) :
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PreintegrationBase(bias,
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measuredAccCovariance, measuredOmegaCovariance,
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integrationErrorCovariance, use2ndOrderIntegration)
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{
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preintMeasCov_.setZero();
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}
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//------------------------------------------------------------------------------
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void ImuFactor::PreintegratedMeasurements::print(const string& s) const {
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PreintegrationBase::print(s);
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cout << " preintMeasCov = \n [ " << preintMeasCov_ << " ]" << endl;
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}
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//------------------------------------------------------------------------------
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bool ImuFactor::PreintegratedMeasurements::equals(const PreintegratedMeasurements& expected, double tol) const {
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return equal_with_abs_tol(preintMeasCov_, expected.preintMeasCov_, tol)
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&& PreintegrationBase::equals(expected, tol);
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}
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//------------------------------------------------------------------------------
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void ImuFactor::PreintegratedMeasurements::resetIntegration(){
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PreintegrationBase::resetIntegration();
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preintMeasCov_.setZero();
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}
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//------------------------------------------------------------------------------
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void ImuFactor::PreintegratedMeasurements::integrateMeasurement(
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const Vector3& measuredAcc, const Vector3& measuredOmega, double deltaT,
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boost::optional<const Pose3&> body_P_sensor,
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OptionalJacobian<9, 9> F_test, OptionalJacobian<9, 9> G_test) {
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Vector3 correctedAcc, correctedOmega;
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correctMeasurementsByBiasAndSensorPose(measuredAcc, measuredOmega, correctedAcc, correctedOmega, body_P_sensor);
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const Vector3 integratedOmega = correctedOmega * deltaT; // rotation vector describing rotation increment computed from the current rotation rate measurement
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Matrix3 D_Rincr_integratedOmega; // Right jacobian computed at theta_incr
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const Rot3 Rincr = Rot3::Expmap(integratedOmega, D_Rincr_integratedOmega); // rotation increment computed from the current rotation rate measurement
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// Update Jacobians
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updatePreintegratedJacobians(correctedAcc, D_Rincr_integratedOmega, Rincr, deltaT);
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// Update preintegrated measurements (also get Jacobian)
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const Matrix3 R_i = deltaRij(); // store this, which is useful to compute G_test
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Matrix9 F; // overall Jacobian wrt preintegrated measurements (df/dx)
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updatePreintegratedMeasurements(correctedAcc, Rincr, deltaT, F);
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// first order covariance propagation:
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// as in [2] we consider a first order propagation that can be seen as a prediction phase in an EKF framework
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/* ----------------------------------------------------------------------------------------------------------------------- */
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// preintMeasCov = F * preintMeasCov * F.transpose() + G * (1/deltaT) * measurementCovariance * G.transpose();
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// NOTE 1: (1/deltaT) allows to pass from continuous time noise to discrete time noise
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// measurementCovariance_discrete = measurementCovariance_contTime * (1/deltaT)
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// NOTE 2: the computation of G * (1/deltaT) * measurementCovariance * G.transpose() is done blockwise,
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// as G and measurementCovariance are blockdiagonal matrices
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preintMeasCov_ = F * preintMeasCov_ * F.transpose();
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preintMeasCov_.block<3,3>(0,0) += integrationCovariance() * deltaT;
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preintMeasCov_.block<3,3>(3,3) += R_i * accelerometerCovariance() * R_i.transpose() * deltaT;
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preintMeasCov_.block<3,3>(6,6) += D_Rincr_integratedOmega * gyroscopeCovariance() * D_Rincr_integratedOmega.transpose() * deltaT;
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Matrix3 F_pos_noiseacc;
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if(use2ndOrderIntegration()){
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F_pos_noiseacc = 0.5 * R_i * deltaT * deltaT;
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preintMeasCov_.block<3,3>(0,0) += (1/deltaT) * F_pos_noiseacc * accelerometerCovariance() * F_pos_noiseacc.transpose();
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Matrix3 temp = F_pos_noiseacc * accelerometerCovariance() * R_i.transpose(); // already includes 1/deltaT
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preintMeasCov_.block<3,3>(0,3) += temp;
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preintMeasCov_.block<3,3>(3,0) += temp.transpose();
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}
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// F_test and G_test are given as output for testing purposes and are not needed by the factor
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if(F_test){ // This in only for testing
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(*F_test) << F;
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}
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if(G_test){ // This in only for testing & documentation, while the actual computation is done block-wise
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if(!use2ndOrderIntegration())
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F_pos_noiseacc = Z_3x3;
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// intNoise accNoise omegaNoise
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(*G_test) << I_3x3 * deltaT, F_pos_noiseacc, Z_3x3, // pos
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Z_3x3, R_i * deltaT, Z_3x3, // vel
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Z_3x3, Z_3x3, D_Rincr_integratedOmega * deltaT; // angle
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}
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}
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//------------------------------------------------------------------------------
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// ImuFactor methods
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//------------------------------------------------------------------------------
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ImuFactor::ImuFactor() :
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ImuFactorBase(), _PIM_(imuBias::ConstantBias(), Z_3x3, Z_3x3, Z_3x3) {}
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//------------------------------------------------------------------------------
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ImuFactor::ImuFactor(
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Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias,
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const PreintegratedMeasurements& preintegratedMeasurements,
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const Vector3& gravity, const Vector3& omegaCoriolis,
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boost::optional<const Pose3&> body_P_sensor,
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const bool use2ndOrderCoriolis) :
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Base(noiseModel::Gaussian::Covariance(preintegratedMeasurements.preintMeasCov_),
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pose_i, vel_i, pose_j, vel_j, bias),
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ImuFactorBase(gravity, omegaCoriolis, body_P_sensor, use2ndOrderCoriolis),
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_PIM_(preintegratedMeasurements) {}
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//------------------------------------------------------------------------------
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gtsam::NonlinearFactor::shared_ptr ImuFactor::clone() const {
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return boost::static_pointer_cast<gtsam::NonlinearFactor>(
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gtsam::NonlinearFactor::shared_ptr(new This(*this)));
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}
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//------------------------------------------------------------------------------
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void ImuFactor::print(const string& s, const KeyFormatter& keyFormatter) const {
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cout << s << "ImuFactor("
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<< keyFormatter(this->key1()) << ","
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<< keyFormatter(this->key2()) << ","
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<< keyFormatter(this->key3()) << ","
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<< keyFormatter(this->key4()) << ","
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<< keyFormatter(this->key5()) << ")\n";
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ImuFactorBase::print("");
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_PIM_.print(" preintegrated measurements:");
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this->noiseModel_->print(" noise model: ");
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}
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//------------------------------------------------------------------------------
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bool ImuFactor::equals(const NonlinearFactor& expected, double tol) const {
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const This *e = dynamic_cast<const This*> (&expected);
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return e != NULL && Base::equals(*e, tol)
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&& _PIM_.equals(e->_PIM_, tol)
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&& ImuFactorBase::equals(*e, tol);
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}
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//------------------------------------------------------------------------------
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Vector ImuFactor::evaluateError(const Pose3& pose_i, const Vector3& vel_i,
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const Pose3& pose_j, const Vector3& vel_j,
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const imuBias::ConstantBias& bias_i, boost::optional<Matrix&> H1,
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boost::optional<Matrix&> H2, boost::optional<Matrix&> H3,
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boost::optional<Matrix&> H4, boost::optional<Matrix&> H5) const {
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return _PIM_.computeErrorAndJacobians(pose_i, vel_i, pose_j, vel_j, bias_i,
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gravity_, omegaCoriolis_, use2ndOrderCoriolis_, H1, H2, H3, H4, H5);
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}
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} /// namespace gtsam
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