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gtsam/gtsam_unstable/slam/CombinedImuFactor.h

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Merged from Hmf6_unordered 970392249a8c50153f24594c9c81acb740cedd06
2013-10-04 03:51:56 +08:00
/* ----------------------------------------------------------------------------
* 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.h
* @author Luca Carlone, Stephen Williams
**/
#pragma once
/* GTSAM includes */
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/navigation/ImuBias.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/LieVector.h>
#include <gtsam/base/debug.h>
/* External or standard includes */
#include <ostream>
namespace gtsam {
/**
*
* @addtogroup SLAM
*
* REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups", Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built
* Environments Without Initial Conditions", TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor: Computation of the Jacobian Matrices", Tech. Report, 2013.
*/
class CombinedImuFactor: public NoiseModelFactor6<Pose3,LieVector,Pose3,LieVector,imuBias::ConstantBias,imuBias::ConstantBias> {
public:
/** Struct to store results of preintegrating IMU measurements. Can be build
* incrementally so as to avoid costly integration at time of factor construction. */
/** Right Jacobian for Exponential map in SO(3) - equation (10.86) and following equations in [1] */
static Matrix3 rightJacobianExpMapSO3(const Vector3& x) {
// x is the axis-angle representation (exponential coordinates) for a rotation
double normx = norm_2(x); // rotation angle
Matrix3 Jr;
if (normx < 10e-8){
Jr = Matrix3::Identity();
}
else{
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
Jr = Matrix3::Identity() - ((1-cos(normx))/(normx*normx)) * X +
((normx-sin(normx))/(normx*normx*normx)) * X * X; // right Jacobian
}
return Jr;
}
/** Right Jacobian for Log map in SO(3) - equation (10.86) and following equations in [1] */
static Matrix3 rightJacobianExpMapSO3inverse(const Vector3& x) {
// x is the axis-angle representation (exponential coordinates) for a rotation
double normx = norm_2(x); // rotation angle
Matrix3 Jrinv;
if (normx < 10e-8){
Jrinv = Matrix3::Identity();
}
else{
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
Jrinv = Matrix3::Identity() +
0.5 * X + (1/(normx*normx) - (1+cos(normx))/(2*normx * sin(normx)) ) * X * X;
}
return Jrinv;
}
/** CombinedPreintegratedMeasurements accumulates (integrates) the IMU measurements (rotation rates and accelerations)
* and the corresponding covariance matrix. The measurements are then used to build the Preintegrated IMU factor*/
class CombinedPreintegratedMeasurements {
public:
imuBias::ConstantBias biasHat; ///< Acceleration and angular rate bias values used during preintegration
Matrix measurementCovariance; ///< (Raw measurements uncertainty) Covariance of the vector
///< [integrationError measuredAcc measuredOmega biasAccRandomWalk biasOmegaRandomWalk biasAccInit biasOmegaInit] in R^(21 x 21)
Vector3 deltaPij; ///< Preintegrated relative position (does not take into account velocity at time i, see deltap+, in [2]) (in frame i)
Vector3 deltaVij; ///< Preintegrated relative velocity (in global frame)
Rot3 deltaRij; ///< Preintegrated relative orientation (in frame i)
double deltaTij; ///< Time interval from i to j
Matrix3 delPdelBiasAcc; ///< Jacobian of preintegrated position w.r.t. acceleration bias
Matrix3 delPdelBiasOmega; ///< Jacobian of preintegrated position w.r.t. angular rate bias
Matrix3 delVdelBiasAcc; ///< Jacobian of preintegrated velocity w.r.t. acceleration bias
Matrix3 delVdelBiasOmega; ///< Jacobian of preintegrated velocity w.r.t. angular rate bias
Matrix3 delRdelBiasOmega; ///< Jacobian of preintegrated rotation w.r.t. angular rate bias
Matrix PreintMeasCov; ///< Covariance matrix of the preintegrated measurements (first-order propagation from *measurementCovariance*)
///< In the combined factor is also includes the biases and keeps the correlation between the preintegrated measurements and the biases
///< COVARIANCE OF: [PreintPOSITION PreintVELOCITY PreintROTATION BiasAcc BiasOmega]
/** Default constructor, initialize with no IMU measurements */
CombinedPreintegratedMeasurements(
const imuBias::ConstantBias& bias, ///< Current estimate of acceleration and rotation rate biases
const Matrix3& measuredAccCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& measuredOmegaCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& integrationErrorCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& biasAccCovariance, ///< Covariance matrix of biasAcc (random walk describing BIAS evolution)
const Matrix3& biasOmegaCovariance, ///< Covariance matrix of biasOmega (random walk describing BIAS evolution)
const Matrix& biasAccOmegaInit ///< Covariance of biasAcc & biasOmega when preintegrating measurements
///< (this allows to consider the uncertainty of the BIAS choice when integrating the measurements)
) : biasHat(bias), measurementCovariance(21,21), deltaPij(Vector3::Zero()), deltaVij(Vector3::Zero()), deltaTij(0.0),
delPdelBiasAcc(Matrix3::Zero()), delPdelBiasOmega(Matrix3::Zero()),
delVdelBiasAcc(Matrix3::Zero()), delVdelBiasOmega(Matrix3::Zero()),
delRdelBiasOmega(Matrix3::Zero()), PreintMeasCov(Matrix::Zero(15,15))
{
tabs converted to two spaces
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// COVARIANCE OF: [Integration AccMeasurement OmegaMeasurement BiasAccRandomWalk BiasOmegaRandomWalk (BiasAccInit BiasOmegaInit)] SIZE (21x21)
measurementCovariance << integrationErrorCovariance , Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), measuredAccCovariance, Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Merged from Hmf6_unordered 970392249a8c50153f24594c9c81acb740cedd06
2013-10-04 03:51:56 +08:00
Matrix3::Zero(), Matrix3::Zero(), measuredOmegaCovariance, Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
tabs converted to two spaces
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Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasAccCovariance, Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasOmegaCovariance, Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasAccOmegaInit.block(0,0,3,3), biasAccOmegaInit.block(0,3,3,3),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasAccOmegaInit.block(3,0,3,3), biasAccOmegaInit.block(3,3,3,3);
Merged from Hmf6_unordered 970392249a8c50153f24594c9c81acb740cedd06
2013-10-04 03:51:56 +08:00
}
CombinedPreintegratedMeasurements() :
biasHat(imuBias::ConstantBias()), measurementCovariance(21,21), deltaPij(Vector3::Zero()), deltaVij(Vector3::Zero()), deltaTij(0.0),
delPdelBiasAcc(Matrix3::Zero()), delPdelBiasOmega(Matrix3::Zero()),
delVdelBiasAcc(Matrix3::Zero()), delVdelBiasOmega(Matrix3::Zero()),
delRdelBiasOmega(Matrix3::Zero()), PreintMeasCov(Matrix::Zero(15,15))
{
}
/** print */
void print(const std::string& s = "Preintegrated Measurements:") const {
std::cout << s << std::endl;
biasHat.print(" biasHat");
std::cout << " deltaTij " << deltaTij << std::endl;
std::cout << " deltaPij [ " << deltaPij.transpose() << " ]" << std::endl;
std::cout << " deltaVij [ " << deltaVij.transpose() << " ]" << std::endl;
deltaRij.print(" deltaRij ");
std::cout << " measurementCovariance [ " << measurementCovariance << " ]" << std::endl;
std::cout << " PreintMeasCov [ " << PreintMeasCov << " ]" << std::endl;
}
/** equals */
bool equals(const CombinedPreintegratedMeasurements& expected, double tol=1e-9) const {
return biasHat.equals(expected.biasHat, tol)
&& equal_with_abs_tol(measurementCovariance, expected.measurementCovariance, tol)
&& equal_with_abs_tol(deltaPij, expected.deltaPij, tol)
&& equal_with_abs_tol(deltaVij, expected.deltaVij, tol)
&& deltaRij.equals(expected.deltaRij, tol)
&& std::fabs(deltaTij - expected.deltaTij) < tol
&& equal_with_abs_tol(delPdelBiasAcc, expected.delPdelBiasAcc, tol)
&& equal_with_abs_tol(delPdelBiasOmega, expected.delPdelBiasOmega, tol)
&& equal_with_abs_tol(delVdelBiasAcc, expected.delVdelBiasAcc, tol)
&& equal_with_abs_tol(delVdelBiasOmega, expected.delVdelBiasOmega, tol)
&& equal_with_abs_tol(delRdelBiasOmega, expected.delRdelBiasOmega, tol);
}
/** Add a single IMU measurement to the preintegration. */
void integrateMeasurement(
const Vector3& measuredAcc, ///< Measured linear acceleration (in body frame)
const Vector3& measuredOmega, ///< Measured angular velocity (in body frame)
double deltaT, ///< Time step
boost::optional<Pose3> body_P_sensor = boost::none ///< Sensor frame
) {
// NOTE: order is important here because each update uses old values, e.g., velocity and position updates are based on previous rotation estimate.
// First we compensate the measurements for the bias: since we have only an estimate of the bias, the covariance includes the corresponding uncertainty
Vector3 correctedAcc = biasHat.correctAccelerometer(measuredAcc);
Vector3 correctedOmega = biasHat.correctGyroscope(measuredOmega);
// Then compensate for sensor-body displacement: we express the quantities (originally in the IMU frame) into the body frame
if(body_P_sensor){
Matrix3 body_R_sensor = body_P_sensor->rotation().matrix();
correctedOmega = body_R_sensor * correctedOmega; // rotation rate vector in the body frame
Matrix3 body_omega_body__cross = skewSymmetric(correctedOmega);
correctedAcc = body_R_sensor * correctedAcc - body_omega_body__cross * body_omega_body__cross * body_P_sensor->translation().vector();
// linear acceleration vector in the body frame
}
const Vector3 theta_incr = correctedOmega * deltaT; // rotation vector describing rotation increment computed from the current rotation rate measurement
const Rot3 Rincr = Rot3::Expmap(theta_incr); // rotation increment computed from the current rotation rate measurement
const Matrix3 Jr_theta_incr = rightJacobianExpMapSO3(theta_incr); // Right jacobian computed at theta_incr
// Update Jacobians
/* ----------------------------------------------------------------------------------------------------------------------- */
delPdelBiasAcc += delVdelBiasAcc * deltaT;
delPdelBiasOmega += delVdelBiasOmega * deltaT;
delVdelBiasAcc += -deltaRij.matrix() * deltaT;
delVdelBiasOmega += -deltaRij.matrix() * skewSymmetric(correctedAcc) * deltaT * delRdelBiasOmega;
delRdelBiasOmega = Rincr.inverse().matrix() * delRdelBiasOmega - Jr_theta_incr * deltaT;
// 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, contrarily to [2] we
// consider the uncertainty of the bias selection and we keep correlation between biases and preintegrated measurements
/* ----------------------------------------------------------------------------------------------------------------------- */
Matrix3 Z_3x3 = Matrix3::Zero();
Matrix3 I_3x3 = Matrix3::Identity();
const Vector3 theta_i = Rot3::Logmap(deltaRij); // parametrization of so(3)
const Matrix3 Jr_theta_i = rightJacobianExpMapSO3(theta_i);
Rot3 Rot_j = deltaRij * Rincr;
const Vector3 theta_j = Rot3::Logmap(Rot_j); // parametrization of so(3)
const Matrix3 Jrinv_theta_j = rightJacobianExpMapSO3inverse(theta_j);
// Single Jacobians to propagate covariance
Matrix3 H_pos_pos = I_3x3;
Matrix3 H_pos_vel = I_3x3 * deltaT;
Matrix3 H_pos_angles = Z_3x3;
Matrix3 H_vel_pos = Z_3x3;
Matrix3 H_vel_vel = I_3x3;
Matrix3 H_vel_angles = - deltaRij.matrix() * skewSymmetric(correctedAcc) * Jr_theta_i * deltaT;
// analytic expression corresponding to the following numerical derivative
// Matrix H_vel_angles = numericalDerivative11<LieVector, LieVector>(boost::bind(&PreIntegrateIMUObservations_delta_vel, correctedOmega, correctedAcc, deltaT, _1, deltaVij), theta_i);
Matrix3 H_vel_biasacc = - deltaRij.matrix() * deltaT;
Matrix3 H_angles_pos = Z_3x3;
Matrix3 H_angles_vel = Z_3x3;
Matrix3 H_angles_angles = Jrinv_theta_j * Rincr.inverse().matrix() * Jr_theta_i;
Matrix3 H_angles_biasomega =- Jrinv_theta_j * Jr_theta_incr * deltaT;
// analytic expression corresponding to the following numerical derivative
// Matrix H_angles_angles = numericalDerivative11<LieVector, LieVector>(boost::bind(&PreIntegrateIMUObservations_delta_angles, correctedOmega, deltaT, _1), thetaij);
// overall Jacobian wrt preintegrated measurements (df/dx)
Matrix F(15,15);
F << H_pos_pos, H_pos_vel, H_pos_angles, Z_3x3, Z_3x3,
tabs converted to two spaces
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H_vel_pos, H_vel_vel, H_vel_angles, H_vel_biasacc, Z_3x3,
H_angles_pos, H_angles_vel, H_angles_angles, Z_3x3, H_angles_biasomega,
Z_3x3, Z_3x3, Z_3x3, I_3x3, Z_3x3,
Z_3x3, Z_3x3, Z_3x3, Z_3x3, I_3x3;
Merged from Hmf6_unordered 970392249a8c50153f24594c9c81acb740cedd06
2013-10-04 03:51:56 +08:00
// first order uncertainty propagation
// Optimized matrix multiplication (1/deltaT) * G * measurementCovariance * G.transpose()
Matrix G_measCov_Gt = Matrix::Zero(15,15);
// BLOCK DIAGONAL TERMS
G_measCov_Gt.block(0,0,3,3) = deltaT * measurementCovariance.block(0,0,3,3);
// G_measCov_Gt.block(3,3,3,3) = (H_vel_biasacc) * (1/deltaT) * measurementCovariance.block(3,3,3,3) * (H_vel_biasacc.transpose()) +
// (H_vel_biasacc) * (1/deltaT) *
// ( measurementCovariance.block(9,9,3,3) + measurementCovariance.block(15,15,3,3) ) *
// (H_vel_biasacc.transpose());
G_measCov_Gt.block(3,3,3,3) = (1/deltaT) * (H_vel_biasacc) *
(measurementCovariance.block(3,3,3,3) + measurementCovariance.block(9,9,3,3) + measurementCovariance.block(15,15,3,3) ) *
(H_vel_biasacc.transpose());
G_measCov_Gt.block(6,6,3,3) = (1/deltaT) * (H_angles_biasomega) *
(measurementCovariance.block(6,6,3,3) + measurementCovariance.block(12,12,3,3) + measurementCovariance.block(18,18,3,3) ) *
(H_angles_biasomega.transpose());
G_measCov_Gt.block(9,9,3,3) = deltaT * measurementCovariance.block(9,9,3,3);
G_measCov_Gt.block(12,12,3,3) = deltaT * measurementCovariance.block(12,12,3,3);
// OFF BLOCK DIAGONAL TERMS
Matrix3 block24 = H_vel_biasacc * measurementCovariance.block(9,9,3,3);
G_measCov_Gt.block(3,9,3,3) = block24;
G_measCov_Gt.block(9,3,3,3) = block24.transpose();
Matrix3 block35 = H_angles_biasomega * measurementCovariance.block(12,12,3,3);
G_measCov_Gt.block(6,12,3,3) = block35;
G_measCov_Gt.block(12,6,3,3) = block35.transpose();
/*
// overall Jacobian wrt raw measurements (df/du)
Matrix3 H_vel_initbiasacc = H_vel_biasacc;
Matrix3 H_angles_initbiasomega = H_angles_biasomega;
// COMBINED IMU FACTOR, preserves correlation with bias evolution and considers initial uncertainty on biases
Matrix G(15,21);
G << I_3x3 * deltaT, Z_3x3, Z_3x3, Z_3x3, Z_3x3, Z_3x3, Z_3x3,
Z_3x3, - H_vel_biasacc, Z_3x3, H_vel_biasacc, Z_3x3, H_vel_initbiasacc, Z_3x3,
Z_3x3, Z_3x3, - H_angles_biasomega, Z_3x3, H_angles_biasomega, Z_3x3, H_angles_initbiasomega,
Z_3x3, Z_3x3, Z_3x3, I_3x3 * deltaT, Z_3x3, Z_3x3, Z_3x3,
Z_3x3, Z_3x3, Z_3x3, Z_3x3, I_3x3 * deltaT, Z_3x3, Z_3x3;
Matrix ErrorMatrix = (1/deltaT) * G * measurementCovariance * G.transpose() - G_measCov_Gt;
std::cout << "---- matrix multiplication error = [" << ErrorMatrix << "];"<< std::endl;
double max_err=0;
for(int i=0;i<15;i++)
{
for(int j=0;j<15;j++)
{
if(fabs(ErrorMatrix(i,j))>max_err)
max_err = fabs(ErrorMatrix(i,j));
}
}
std::cout << "---- max matrix multiplication error = [" << max_err << "];"<< std::endl;
if(max_err>10e-15)
std::cout << "---- max matrix multiplication error *large* = [" << max_err << "];"<< std::endl;
PreintMeasCov = F * PreintMeasCov * F.transpose() + (1/deltaT) * G * measurementCovariance * G.transpose();
*/
PreintMeasCov = F * PreintMeasCov * F.transpose() + G_measCov_Gt;
// Update preintegrated measurements
/* ----------------------------------------------------------------------------------------------------------------------- */
deltaPij += deltaVij * deltaT;
deltaVij += deltaRij.matrix() * correctedAcc * deltaT;
deltaRij = deltaRij * Rincr;
deltaTij += deltaT;
}
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
// This function is only used for test purposes (compare numerical derivatives wrt analytic ones)
static inline Vector PreIntegrateIMUObservations_delta_vel(const Vector& msr_gyro_t, const Vector& msr_acc_t, const double msr_dt,
const Vector3& delta_angles, const Vector& delta_vel_in_t0){
// Note: all delta terms refer to an IMU\sensor system at t0
Vector body_t_a_body = msr_acc_t;
Rot3 R_t_to_t0 = Rot3::Expmap(delta_angles);
return delta_vel_in_t0 + R_t_to_t0.matrix() * body_t_a_body * msr_dt;
}
// This function is only used for test purposes (compare numerical derivatives wrt analytic ones)
static inline Vector PreIntegrateIMUObservations_delta_angles(const Vector& msr_gyro_t, const double msr_dt,
const Vector3& delta_angles){
// Note: all delta terms refer to an IMU\sensor system at t0
// Calculate the corrected measurements using the Bias object
Vector body_t_omega_body= msr_gyro_t;
Rot3 R_t_to_t0 = Rot3::Expmap(delta_angles);
R_t_to_t0 = R_t_to_t0 * Rot3::Expmap( body_t_omega_body*msr_dt );
return Rot3::Logmap(R_t_to_t0);
}
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_NVP(biasHat);
ar & BOOST_SERIALIZATION_NVP(measurementCovariance);
ar & BOOST_SERIALIZATION_NVP(deltaPij);
ar & BOOST_SERIALIZATION_NVP(deltaVij);
ar & BOOST_SERIALIZATION_NVP(deltaRij);
ar & BOOST_SERIALIZATION_NVP(deltaTij);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasAcc);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasOmega);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasAcc);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasOmega);
ar & BOOST_SERIALIZATION_NVP(delRdelBiasOmega);
}
};
private:
typedef CombinedImuFactor This;
typedef NoiseModelFactor6<Pose3,LieVector,Pose3,LieVector,imuBias::ConstantBias,imuBias::ConstantBias> Base;
CombinedPreintegratedMeasurements preintegratedMeasurements_;
Vector3 gravity_;
Vector3 omegaCoriolis_;
public:
/** Shorthand for a smart pointer to a factor */
#ifndef _MSC_VER
typedef typename boost::shared_ptr<CombinedImuFactor> shared_ptr;
#else
typedef boost::shared_ptr<CombinedImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
CombinedImuFactor() : preintegratedMeasurements_(imuBias::ConstantBias(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix::Zero(6,6)) {}
/** Constructor */
CombinedImuFactor(Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias_i, Key bias_j,
const CombinedPreintegratedMeasurements& preintegratedMeasurements, const Vector3& gravity, const Vector3& omegaCoriolis,
const SharedNoiseModel& model) :
Base(model, pose_i, vel_i, pose_j, vel_j, bias_i, bias_j),
preintegratedMeasurements_(preintegratedMeasurements),
gravity_(gravity),
omegaCoriolis_(omegaCoriolis) {
}
virtual ~CombinedImuFactor() {}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new This(*this))); }
/** implement functions needed for Testable */
/** print */
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << "CombinedImuFactor("
<< keyFormatter(this->key1()) << ","
<< keyFormatter(this->key2()) << ","
<< keyFormatter(this->key3()) << ","
<< keyFormatter(this->key4()) << ","
<< keyFormatter(this->key5()) << ","
<< keyFormatter(this->key6()) << ")\n";
preintegratedMeasurements_.print(" preintegrated measurements:");
std::cout << " gravity: [ " << gravity_.transpose() << " ]" << std::endl;
std::cout << " omegaCoriolis: [ " << omegaCoriolis_.transpose() << " ]" << std::endl;
this->noiseModel_->print(" noise model: ");
}
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL && Base::equals(*e, tol)
&& preintegratedMeasurements_.equals(e->preintegratedMeasurements_)
&& equal_with_abs_tol(gravity_, e->gravity_, tol)
&& equal_with_abs_tol(omegaCoriolis_, e->omegaCoriolis_, tol);
}
/** Access the preintegrated measurements. */
const CombinedPreintegratedMeasurements& preintegratedMeasurements() const {
return preintegratedMeasurements_; }
/** implement functions needed to derive from Factor */
/** vector of errors */
Vector evaluateError(const Pose3& pose_i, const LieVector& vel_i, const Pose3& pose_j, const LieVector& vel_j,
const imuBias::ConstantBias& bias_i, const imuBias::ConstantBias& bias_j,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none,
boost::optional<Matrix&> H4 = boost::none,
boost::optional<Matrix&> H5 = boost::none,
boost::optional<Matrix&> H6 = boost::none) const
{
const double& deltaTij = preintegratedMeasurements_.deltaTij;
const Vector3 biasAccIncr = bias_i.accelerometer() - preintegratedMeasurements_.biasHat.accelerometer();
const Vector3 biasOmegaIncr = bias_i.gyroscope() - preintegratedMeasurements_.biasHat.gyroscope();
// we give some shorter name to rotations and translations
const Rot3 Rot_i = pose_i.rotation();
const Rot3 Rot_j = pose_j.rotation();
const Vector3 pos_i = pose_i.translation().vector();
const Vector3 pos_j = pose_j.translation().vector();
// We compute factor's Jacobians, according to [3]
/* ---------------------------------------------------------------------------------------------------- */
const Rot3 deltaRij_biascorrected = preintegratedMeasurements_.deltaRij.retract(preintegratedMeasurements_.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);
// deltaRij_biascorrected is expmap(deltaRij) * expmap(delRdelBiasOmega * biasOmegaIncr)
Vector3 theta_biascorrected = Rot3::Logmap(deltaRij_biascorrected);
Vector3 theta_biascorrected_corioliscorrected = theta_biascorrected -
Rot_i.inverse().matrix() * omegaCoriolis_ * deltaTij; // Coriolis term
const Rot3 deltaRij_biascorrected_corioliscorrected =
Rot3::Expmap( theta_biascorrected_corioliscorrected );
const Rot3 fRhat = deltaRij_biascorrected_corioliscorrected.between(Rot_i.between(Rot_j));
const Matrix3 Jr_theta_bcc = rightJacobianExpMapSO3(theta_biascorrected_corioliscorrected);
const Matrix3 Jtheta = -Jr_theta_bcc * skewSymmetric(Rot_i.inverse().matrix() * omegaCoriolis_ * deltaTij);
const Matrix3 Jrinv_fRhat = rightJacobianExpMapSO3inverse(Rot3::Logmap(fRhat));
if(H1) {
H1->resize(15,6);
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
- Rot_i.matrix(),
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
Matrix3::Zero(),
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero(),
//dBiasAcc/dPi
Matrix3::Zero(), Matrix3::Zero(),
//dBiasOmega/dPi
Matrix3::Zero(), Matrix3::Zero();
}
if(H2) {
H2->resize(15,3);
(*H2) <<
// dfP/dVi
- Matrix3::Identity() * deltaTij
+ skewSymmetric(omegaCoriolis_) * deltaTij * deltaTij, // Coriolis term - we got rid of the 2 wrt ins paper
// dfV/dVi
- Matrix3::Identity()
+ 2 * skewSymmetric(omegaCoriolis_) * deltaTij, // Coriolis term
// dfR/dVi
Matrix3::Zero(),
//dBiasAcc/dVi
Matrix3::Zero(),
//dBiasOmega/dVi
Matrix3::Zero();
}
if(H3) {
H3->resize(15,6);
(*H3) <<
// dfP/dPosej
Matrix3::Zero(), Rot_j.matrix(),
// dfV/dPosej
Matrix::Zero(3,6),
// dfR/dPosej
Jrinv_fRhat * ( Matrix3::Identity() ), Matrix3::Zero(),
//dBiasAcc/dPosej
Matrix3::Zero(), Matrix3::Zero(),
//dBiasOmega/dPosej
Matrix3::Zero(), Matrix3::Zero();
}
if(H4) {
H4->resize(15,3);
(*H4) <<
// dfP/dVj
Matrix3::Zero(),
// dfV/dVj
Matrix3::Identity(),
// dfR/dVj
Matrix3::Zero(),
//dBiasAcc/dVj
Matrix3::Zero(),
//dBiasOmega/dVj
Matrix3::Zero();
}
if(H5) {
const Matrix3 Jrinv_theta_bc = rightJacobianExpMapSO3inverse(theta_biascorrected);
const Matrix3 Jr_JbiasOmegaIncr = rightJacobianExpMapSO3(preintegratedMeasurements_.delRdelBiasOmega * biasOmegaIncr);
const Matrix3 JbiasOmega = Jr_theta_bcc * Jrinv_theta_bc * Jr_JbiasOmegaIncr * preintegratedMeasurements_.delRdelBiasOmega;
H5->resize(15,6);
(*H5) <<
// dfP/dBias_i
- Rot_i.matrix() * preintegratedMeasurements_.delPdelBiasAcc,
- Rot_i.matrix() * preintegratedMeasurements_.delPdelBiasOmega,
// dfV/dBias_i
- Rot_i.matrix() * preintegratedMeasurements_.delVdelBiasAcc,
- Rot_i.matrix() * preintegratedMeasurements_.delVdelBiasOmega,
// dfR/dBias_i
Matrix::Zero(3,3),
Jrinv_fRhat * ( - fRhat.inverse().matrix() * JbiasOmega),
//dBiasAcc/dBias_i
-Matrix3::Identity(), Matrix3::Zero(),
//dBiasOmega/dBias_i
Matrix3::Zero(), -Matrix3::Identity();
}
if(H6) {
tabs converted to two spaces
2013-10-11 01:52:57 +08:00
H6->resize(15,6);
(*H6) <<
// dfP/dBias_j
Matrix3::Zero(), Matrix3::Zero(),
// dfV/dBias_j
Matrix3::Zero(), Matrix3::Zero(),
// dfR/dBias_j
Matrix3::Zero(), Matrix3::Zero(),
//dBiasAcc/dBias_j
Matrix3::Identity(), Matrix3::Zero(),
//dBiasOmega/dBias_j
Matrix3::Zero(), Matrix3::Identity();
Merged from Hmf6_unordered 970392249a8c50153f24594c9c81acb740cedd06
2013-10-04 03:51:56 +08:00
}
// Evaluate residual error, according to [3]
/* ---------------------------------------------------------------------------------------------------- */
const Vector3 fp =
pos_j - pos_i
- Rot_i.matrix() * (preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr)
- vel_i * deltaTij
+ skewSymmetric(omegaCoriolis_) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
- 0.5 * gravity_ * deltaTij*deltaTij;
const Vector3 fv =
vel_j - vel_i - Rot_i.matrix() * (preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr)
+ 2 * skewSymmetric(omegaCoriolis_) * vel_i * deltaTij // Coriolis term
- gravity_ * deltaTij;
const Vector3 fR = Rot3::Logmap(fRhat);
const Vector3 fbiasAcc = bias_j.accelerometer() - bias_i.accelerometer();
const Vector3 fbiasOmega = bias_j.gyroscope() - bias_i.gyroscope();
Vector r(15); r << fp, fv, fR, fbiasAcc, fbiasOmega; // vector of size 15
return r;
}
/** predicted states from IMU */
static void Predict(const Pose3& pose_i, const LieVector& vel_i, Pose3& pose_j, LieVector& vel_j,
const imuBias::ConstantBias& bias_i, imuBias::ConstantBias& bias_j,
const CombinedPreintegratedMeasurements preintegratedMeasurements,
const Vector3& gravity, const Vector3& omegaCoriolis)
{
const double& deltaTij = preintegratedMeasurements.deltaTij;
const Vector3 biasAccIncr = bias_i.accelerometer() - preintegratedMeasurements.biasHat.accelerometer();
const Vector3 biasOmegaIncr = bias_i.gyroscope() - preintegratedMeasurements.biasHat.gyroscope();
const Rot3 Rot_i = pose_i.rotation();
const Vector3 pos_i = pose_i.translation().vector();
// Predict state at time j
/* ---------------------------------------------------------------------------------------------------- */
const Vector3 pos_j = pos_i + Rot_i.matrix() * (preintegratedMeasurements.deltaPij
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
vel_j = LieVector(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
const Rot3 deltaRij_biascorrected = preintegratedMeasurements.deltaRij.retract(preintegratedMeasurements.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);
// deltaRij_biascorrected is expmap(deltaRij) * expmap(delRdelBiasOmega * biasOmegaIncr)
Vector3 theta_biascorrected = Rot3::Logmap(deltaRij_biascorrected);
Vector3 theta_biascorrected_corioliscorrected = theta_biascorrected -
Rot_i.inverse().matrix() * omegaCoriolis * deltaTij; // Coriolis term
const Rot3 deltaRij_biascorrected_corioliscorrected =
Rot3::Expmap( theta_biascorrected_corioliscorrected );
const Rot3 Rot_j = Rot_i.compose( deltaRij_biascorrected_corioliscorrected );
pose_j = Pose3( Rot_j, Point3(pos_j) );
bias_j = bias_i;
}
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("NoiseModelFactor6",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(preintegratedMeasurements_);
ar & BOOST_SERIALIZATION_NVP(gravity_);
ar & BOOST_SERIALIZATION_NVP(omegaCoriolis_);
}
}; // \class CombinedImuFactor
} /// namespace gtsam