12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
gtsam/gtsam/navigation/CombinedImuFactor.cpp

319 lines
12 KiB
C++
Raw Blame History

/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file CombinedImuFactor.cpp
* @author Luca Carlone
* @author Stephen Williams
* @author Richard Roberts
* @author Vadim Indelman
* @author David Jensen
* @author Frank Dellaert
* @author Varun Agrawal
**/
#include <gtsam/navigation/CombinedImuFactor.h>
#include <boost/serialization/export.hpp>
/* External or standard includes */
#include <ostream>
namespace gtsam {
using namespace std;
//------------------------------------------------------------------------------
// Inner class PreintegrationCombinedParams
//------------------------------------------------------------------------------
void PreintegrationCombinedParams::print(const string& s) const {
PreintegrationParams::print(s);
cout << "biasAccCovariance:\n[\n" << biasAccCovariance << "\n]"
<< endl;
cout << "biasOmegaCovariance:\n[\n" << biasOmegaCovariance << "\n]"
<< endl;
cout << "biasAccOmegaInt:\n[\n" << biasAccOmegaInt << "\n]"
<< endl;
}
//------------------------------------------------------------------------------
bool PreintegrationCombinedParams::equals(const PreintegratedRotationParams& other,
double tol) const {
auto e = dynamic_cast<const PreintegrationCombinedParams*>(&other);
return e != nullptr && PreintegrationParams::equals(other, tol) &&
equal_with_abs_tol(biasAccCovariance, e->biasAccCovariance,
tol) &&
equal_with_abs_tol(biasOmegaCovariance, e->biasOmegaCovariance,
tol) &&
equal_with_abs_tol(biasAccOmegaInt, e->biasAccOmegaInt, tol);
}
//------------------------------------------------------------------------------
// Inner class PreintegratedCombinedMeasurements
//------------------------------------------------------------------------------
void PreintegratedCombinedMeasurements::print(const string& s) const {
PreintegrationType::print(s);
cout << " preintMeasCov [ " << preintMeasCov_ << " ]" << endl;
}
//------------------------------------------------------------------------------
bool PreintegratedCombinedMeasurements::equals(
const PreintegratedCombinedMeasurements& other, double tol) const {
return PreintegrationType::equals(other, tol)
&& equal_with_abs_tol(preintMeasCov_, other.preintMeasCov_, tol);
}
//------------------------------------------------------------------------------
void PreintegratedCombinedMeasurements::resetIntegration() {
PreintegrationType::resetIntegration();
preintMeasCov_.setZero();
}
//------------------------------------------------------------------------------
// sugar for derivative blocks
#define D_R_R(H) (H)->block<3,3>(0,0)
#define D_R_t(H) (H)->block<3,3>(0,3)
#define D_R_v(H) (H)->block<3,3>(0,6)
#define D_t_R(H) (H)->block<3,3>(3,0)
#define D_t_t(H) (H)->block<3,3>(3,3)
#define D_t_v(H) (H)->block<3,3>(3,6)
#define D_v_R(H) (H)->block<3,3>(6,0)
#define D_v_t(H) (H)->block<3,3>(6,3)
#define D_v_v(H) (H)->block<3,3>(6,6)
#define D_a_a(H) (H)->block<3,3>(9,9)
#define D_g_g(H) (H)->block<3,3>(12,12)
//------------------------------------------------------------------------------
void PreintegratedCombinedMeasurements::integrateMeasurement(
const Vector3& measuredAcc, const Vector3& measuredOmega, double dt) {
// Update preintegrated measurements.
Matrix9 A; // overall Jacobian wrt preintegrated measurements (df/dx)
Matrix93 B, C;
PreintegrationType::update(measuredAcc, measuredOmega, dt, &A, &B, &C);
// Update preintegrated measurements covariance: as in [2] we consider a first
// order propagation that can be seen as a prediction phase in an EKF
// framework. In this implementation, in contrast to [2], we consider the
// uncertainty of the bias selection and we keep correlation between biases
// and preintegrated measurements
// Single Jacobians to propagate covariance
// TODO(frank): should we not also account for bias on position?
Matrix3 theta_H_biasOmega = -C.topRows<3>();
Matrix3 vel_H_biasAcc = -B.bottomRows<3>();
// overall Jacobian wrt preintegrated measurements (df/dx)
Eigen::Matrix<double, 15, 15> F;
F.setZero();
F.block<9, 9>(0, 0) = A;
F.block<3, 3>(0, 12) = theta_H_biasOmega;
F.block<3, 3>(6, 9) = vel_H_biasAcc;
F.block<6, 6>(9, 9) = I_6x6;
// propagate uncertainty
// TODO(frank): use noiseModel routine so we can have arbitrary noise models.
const Matrix3& aCov = p().accelerometerCovariance;
const Matrix3& wCov = p().gyroscopeCovariance;
const Matrix3& iCov = p().integrationCovariance;
// first order uncertainty propagation
// Optimized matrix multiplication (1/dt) * G * measurementCovariance *
// G.transpose()
Eigen::Matrix<double, 15, 15> G_measCov_Gt;
G_measCov_Gt.setZero(15, 15);
// BLOCK DIAGONAL TERMS
D_t_t(&G_measCov_Gt) = dt * iCov;
D_v_v(&G_measCov_Gt) = (1 / dt) * vel_H_biasAcc
* (aCov + p().biasAccOmegaInt.block<3, 3>(0, 0))
* (vel_H_biasAcc.transpose());
D_R_R(&G_measCov_Gt) = (1 / dt) * theta_H_biasOmega
* (wCov + p().biasAccOmegaInt.block<3, 3>(3, 3))
* (theta_H_biasOmega.transpose());
D_a_a(&G_measCov_Gt) = dt * p().biasAccCovariance;
D_g_g(&G_measCov_Gt) = dt * p().biasOmegaCovariance;
// OFF BLOCK DIAGONAL TERMS
Matrix3 temp = vel_H_biasAcc * p().biasAccOmegaInt.block<3, 3>(3, 0)
* theta_H_biasOmega.transpose();
D_v_R(&G_measCov_Gt) = temp;
D_R_v(&G_measCov_Gt) = temp.transpose();
preintMeasCov_ = F * preintMeasCov_ * F.transpose() + G_measCov_Gt;
}
//------------------------------------------------------------------------------
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
PreintegratedCombinedMeasurements::PreintegratedCombinedMeasurements(
const imuBias::ConstantBias& biasHat, const Matrix3& measuredAccCovariance,
const Matrix3& measuredOmegaCovariance,
const Matrix3& integrationErrorCovariance, const Matrix3& biasAccCovariance,
const Matrix3& biasOmegaCovariance, const Matrix6& biasAccOmegaInt,
const bool use2ndOrderIntegration) {
if (!use2ndOrderIntegration)
throw("PreintegratedImuMeasurements no longer supports first-order integration: it incorrectly compensated for gravity");
biasHat_ = biasHat;
boost::shared_ptr<Params> p = Params::MakeSharedD();
p->gyroscopeCovariance = measuredOmegaCovariance;
p->accelerometerCovariance = measuredAccCovariance;
p->integrationCovariance = integrationErrorCovariance;
p->biasAccCovariance = biasAccCovariance;
p->biasOmegaCovariance = biasOmegaCovariance;
p->biasAccOmegaInt = biasAccOmegaInt;
p_ = p;
resetIntegration();
preintMeasCov_.setZero();
}
#endif
//------------------------------------------------------------------------------
// CombinedImuFactor methods
//------------------------------------------------------------------------------
CombinedImuFactor::CombinedImuFactor(Key pose_i, Key vel_i, Key pose_j,
Key vel_j, Key bias_i, Key bias_j,
const PreintegratedCombinedMeasurements& pim) :
Base(noiseModel::Gaussian::Covariance(pim.preintMeasCov_), pose_i, vel_i,
pose_j, vel_j, bias_i, bias_j), _PIM_(pim) {
}
//------------------------------------------------------------------------------
gtsam::NonlinearFactor::shared_ptr CombinedImuFactor::clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new This(*this)));
}
//------------------------------------------------------------------------------
void CombinedImuFactor::print(const string& s,
const KeyFormatter& keyFormatter) const {
cout << s << "CombinedImuFactor(" << keyFormatter(this->key1()) << ","
<< keyFormatter(this->key2()) << "," << keyFormatter(this->key3()) << ","
<< keyFormatter(this->key4()) << "," << keyFormatter(this->key5()) << ","
<< keyFormatter(this->key6()) << ")\n";
_PIM_.print(" preintegrated measurements:");
this->noiseModel_->print(" noise model: ");
}
//------------------------------------------------------------------------------
bool CombinedImuFactor::equals(const NonlinearFactor& other, double tol) const {
const This* e = dynamic_cast<const This*>(&other);
return e != nullptr && Base::equals(*e, tol) && _PIM_.equals(e->_PIM_, tol);
}
//------------------------------------------------------------------------------
Vector CombinedImuFactor::evaluateError(const Pose3& pose_i,
const Vector3& vel_i, const Pose3& pose_j, const Vector3& vel_j,
const imuBias::ConstantBias& bias_i, const imuBias::ConstantBias& bias_j,
boost::optional<Matrix&> H1, boost::optional<Matrix&> H2,
boost::optional<Matrix&> H3, boost::optional<Matrix&> H4,
boost::optional<Matrix&> H5, boost::optional<Matrix&> H6) const {
// error wrt bias evolution model (random walk)
Matrix6 Hbias_i, Hbias_j;
Vector6 fbias = traits<imuBias::ConstantBias>::Between(bias_j, bias_i,
H6 ? &Hbias_j : 0, H5 ? &Hbias_i : 0).vector();
Matrix96 D_r_pose_i, D_r_pose_j, D_r_bias_i;
Matrix93 D_r_vel_i, D_r_vel_j;
// error wrt preintegrated measurements
Vector9 r_Rpv = _PIM_.computeErrorAndJacobians(pose_i, vel_i, pose_j, vel_j,
bias_i, H1 ? &D_r_pose_i : 0, H2 ? &D_r_vel_i : 0, H3 ? &D_r_pose_j : 0,
H4 ? &D_r_vel_j : 0, H5 ? &D_r_bias_i : 0);
// if we need the jacobians
if (H1) {
H1->resize(15, 6);
H1->block<9, 6>(0, 0) = D_r_pose_i;
// adding: [dBiasAcc/dPi ; dBiasOmega/dPi]
H1->block<6, 6>(9, 0).setZero();
}
if (H2) {
H2->resize(15, 3);
H2->block<9, 3>(0, 0) = D_r_vel_i;
// adding: [dBiasAcc/dVi ; dBiasOmega/dVi]
H2->block<6, 3>(9, 0).setZero();
}
if (H3) {
H3->resize(15, 6);
H3->block<9, 6>(0, 0) = D_r_pose_j;
// adding: [dBiasAcc/dPj ; dBiasOmega/dPj]
H3->block<6, 6>(9, 0).setZero();
}
if (H4) {
H4->resize(15, 3);
H4->block<9, 3>(0, 0) = D_r_vel_j;
// adding: [dBiasAcc/dVi ; dBiasOmega/dVi]
H4->block<6, 3>(9, 0).setZero();
}
if (H5) {
H5->resize(15, 6);
H5->block<9, 6>(0, 0) = D_r_bias_i;
// adding: [dBiasAcc/dBias_i ; dBiasOmega/dBias_i]
H5->block<6, 6>(9, 0) = Hbias_i;
}
if (H6) {
H6->resize(15, 6);
H6->block<9, 6>(0, 0).setZero();
// adding: [dBiasAcc/dBias_j ; dBiasOmega/dBias_j]
H6->block<6, 6>(9, 0) = Hbias_j;
}
// overall error
Vector r(15);
r << r_Rpv, fbias; // vector of size 15
return r;
}
//------------------------------------------------------------------------------
std::ostream& operator<<(std::ostream& os, const CombinedImuFactor& f) {
f._PIM_.print("combined preintegrated measurements:\n");
os << " noise model sigmas: " << f.noiseModel_->sigmas().transpose();
return os;
}
//------------------------------------------------------------------------------
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
CombinedImuFactor::CombinedImuFactor(
Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias_i, Key bias_j,
const CombinedPreintegratedMeasurements& pim, const Vector3& n_gravity,
const Vector3& omegaCoriolis, const boost::optional<Pose3>& body_P_sensor,
const bool use2ndOrderCoriolis)
: Base(noiseModel::Gaussian::Covariance(pim.preintMeasCov_), pose_i, vel_i,
pose_j, vel_j, bias_i, bias_j),
_PIM_(pim) {
using P = CombinedPreintegratedMeasurements::Params;
auto p = boost::allocate_shared<P>(Eigen::aligned_allocator<P>(), pim.p());
p->n_gravity = n_gravity;
p->omegaCoriolis = omegaCoriolis;
p->body_P_sensor = body_P_sensor;
p->use2ndOrderCoriolis = use2ndOrderCoriolis;
_PIM_.p_ = p;
}
void CombinedImuFactor::Predict(const Pose3& pose_i, const Vector3& vel_i,
Pose3& pose_j, Vector3& vel_j,
const imuBias::ConstantBias& bias_i,
CombinedPreintegratedMeasurements& pim,
const Vector3& n_gravity,
const Vector3& omegaCoriolis,
const bool use2ndOrderCoriolis) {
// use deprecated predict
PoseVelocityBias pvb = pim.predict(pose_i, vel_i, bias_i, n_gravity,
omegaCoriolis, use2ndOrderCoriolis);
pose_j = pvb.pose;
vel_j = pvb.velocity;
}
#endif
}
/// namespace gtsam
/// Boost serialization export definition for derived class
BOOST_CLASS_EXPORT_IMPLEMENT(gtsam::PreintegrationCombinedParams);
Reference in New Issue View Git Blame Copy Permalink