gtsam/gtsam/navigation/ImuFactor.h

351 lines
12 KiB
C++

/* ----------------------------------------------------------------------------
* 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 ImuFactor.h
* @author Luca Carlone
* @author Stephen Williams
* @author Richard Roberts
* @author Vadim Indelman
* @author David Jensen
* @author Frank Dellaert
**/
#pragma once
/* GTSAM includes */
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/navigation/PreintegrationBase.h>
#include <gtsam/base/debug.h>
namespace gtsam {
/*
* If you are using the factor, please cite:
* L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, "Eliminating
* conditionally independent sets in factor graphs: a unifying perspective based
* on smart factors", Int. Conf. on Robotics and Automation (ICRA), 2014.
*
* C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, "IMU Preintegration on
* Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation",
* Robotics: Science and Systems (RSS), 2015.
*
* 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.
* [4] C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, "IMU Preintegration on
* Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation",
* Robotics: Science and Systems (RSS), 2015.
*/
/**
* PreintegratedIMUMeasurements 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.
* Integration is done incrementally (ideally, one integrates the measurement
* as soon as it is received from the IMU) so as to avoid costly integration
* at time of factor construction.
*
* @addtogroup SLAM
*/
class PreintegratedImuMeasurements: public PreintegrationBase {
friend class ImuFactor;
friend class ImuFactor2;
protected:
Matrix9 preintMeasCov_; ///< COVARIANCE OF: [PreintPOSITION PreintVELOCITY PreintROTATION]
///< (first-order propagation from *measurementCovariance*).
/// Default constructor for serialization
PreintegratedImuMeasurements() {
preintMeasCov_.setZero();
}
public:
/**
* Constructor, initializes the class with no measurements
* @param bias Current estimate of acceleration and rotation rate biases
* @param p Parameters, typically fixed in a single application
*/
PreintegratedImuMeasurements(const boost::shared_ptr<PreintegrationParams>& p,
const imuBias::ConstantBias& biasHat = imuBias::ConstantBias()) :
PreintegrationBase(p, biasHat) {
preintMeasCov_.setZero();
}
/**
* Construct preintegrated directly from members: base class and preintMeasCov
* @param base PreintegrationBase instance
* @param preintMeasCov Covariance matrix used in noise model.
*/
PreintegratedImuMeasurements(const PreintegrationBase& base, const Matrix9& preintMeasCov)
: PreintegrationBase(base),
preintMeasCov_(preintMeasCov) {
}
/// print
void print(const std::string& s = "Preintegrated Measurements:") const;
/// equals
bool equals(const PreintegratedImuMeasurements& expected,
double tol = 1e-9) const;
/// Re-initialize PreintegratedIMUMeasurements
void resetIntegration();
/**
* Add a single IMU measurement to the preintegration.
* @param measuredAcc Measured acceleration (in body frame, as given by the sensor)
* @param measuredOmega Measured angular velocity (as given by the sensor)
* @param dt Time interval between this and the last IMU measurement
*/
void integrateMeasurement(const Vector3& measuredAcc, const Vector3& measuredOmega, double dt);
/// Add multiple measurements, in matrix columns
void integrateMeasurements(const Matrix& measuredAccs, const Matrix& measuredOmegas,
const Matrix& dts);
/// Return pre-integrated measurement covariance
Matrix preintMeasCov() const { return preintMeasCov_; }
/// Merge in a different set of measurements and update bias derivatives accordingly
void mergeWith(const PreintegratedImuMeasurements& pim, Matrix9* H1, Matrix9* H2);
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @deprecated constructor
/// NOTE(frank): assumes Z-Down convention, only second order integration supported
PreintegratedImuMeasurements(const imuBias::ConstantBias& biasHat,
const Matrix3& measuredAccCovariance,
const Matrix3& measuredOmegaCovariance,
const Matrix3& integrationErrorCovariance,
bool use2ndOrderIntegration = true);
/// @deprecated version of integrateMeasurement with body_P_sensor
/// Use parameters instead
void integrateMeasurement(const Vector3& measuredAcc,
const Vector3& measuredOmega, double dt,
boost::optional<Pose3> body_P_sensor);
#endif
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(PreintegrationBase);
ar & bs::make_nvp("preintMeasCov_", bs::make_array(preintMeasCov_.data(), preintMeasCov_.size()));
}
};
/**
* ImuFactor is a 5-ways factor involving previous state (pose and velocity of
* the vehicle at previous time step), current state (pose and velocity at
* current time step), and the bias estimate. Following the preintegration
* scheme proposed in [2], the ImuFactor includes many IMU measurements, which
* are "summarized" using the PreintegratedIMUMeasurements class.
* Note that this factor does not model "temporal consistency" of the biases
* (which are usually slowly varying quantities), which is up to the caller.
* See also CombinedImuFactor for a class that does this for you.
*
* @addtogroup SLAM
*/
class ImuFactor: public NoiseModelFactor5<Pose3, Vector3, Pose3, Vector3,
imuBias::ConstantBias> {
private:
typedef ImuFactor This;
typedef NoiseModelFactor5<Pose3, Vector3, Pose3, Vector3,
imuBias::ConstantBias> Base;
PreintegratedImuMeasurements _PIM_;
public:
/** Shorthand for a smart pointer to a factor */
#if !defined(_MSC_VER) && __GNUC__ == 4 && __GNUC_MINOR__ > 5
typedef typename boost::shared_ptr<ImuFactor> shared_ptr;
#else
typedef boost::shared_ptr<ImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
ImuFactor() {}
/**
* Constructor
* @param pose_i Previous pose key
* @param vel_i Previous velocity key
* @param pose_j Current pose key
* @param vel_j Current velocity key
* @param bias Previous bias key
*/
ImuFactor(Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias,
const PreintegratedImuMeasurements& preintegratedMeasurements);
virtual ~ImuFactor() {
}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const;
/// @name Testable
/// @{
GTSAM_EXPORT friend std::ostream& operator<<(std::ostream& os, const ImuFactor&);
virtual void print(const std::string& s, const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const;
virtual bool equals(const NonlinearFactor& expected, double tol = 1e-9) const;
/// @}
/** Access the preintegrated measurements. */
const PreintegratedImuMeasurements& preintegratedMeasurements() const {
return _PIM_;
}
/** implement functions needed to derive from Factor */
/// vector of errors
Vector evaluateError(const Pose3& pose_i, const Vector3& vel_i,
const Pose3& pose_j, const Vector3& vel_j,
const imuBias::ConstantBias& bias_i, 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) const;
/// Merge two pre-integrated measurement classes
static PreintegratedImuMeasurements Merge(
const PreintegratedImuMeasurements& pim01,
const PreintegratedImuMeasurements& pim12);
/// Merge two factors
static shared_ptr Merge(const shared_ptr& f01, const shared_ptr& f12);
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// @deprecated typename
typedef PreintegratedImuMeasurements PreintegratedMeasurements;
/// @deprecated constructor, in the new one gravity, coriolis settings are in PreintegrationParams
ImuFactor(Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias,
const PreintegratedMeasurements& preintegratedMeasurements,
const Vector3& n_gravity, const Vector3& omegaCoriolis,
const boost::optional<Pose3>& body_P_sensor = boost::none,
const bool use2ndOrderCoriolis = false);
/// @deprecated use PreintegrationBase::predict,
/// in the new one gravity, coriolis settings are in PreintegrationParams
static void Predict(const Pose3& pose_i, const Vector3& vel_i, Pose3& pose_j,
Vector3& vel_j, const imuBias::ConstantBias& bias_i,
PreintegratedMeasurements& pim, const Vector3& n_gravity,
const Vector3& omegaCoriolis, const bool use2ndOrderCoriolis = false);
#endif
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & boost::serialization::make_nvp("NoiseModelFactor5",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(_PIM_);
}
};
// class ImuFactor
/**
* ImuFactor2 is a ternary factor that uses NavStates rather than Pose/Velocity.
* @addtogroup SLAM
*/
class ImuFactor2 : public NoiseModelFactor3<NavState, NavState, imuBias::ConstantBias> {
private:
typedef ImuFactor2 This;
typedef NoiseModelFactor3<NavState, NavState, imuBias::ConstantBias> Base;
PreintegratedImuMeasurements _PIM_;
public:
/** Default constructor - only use for serialization */
ImuFactor2() {}
/**
* Constructor
* @param state_i Previous state key
* @param state_j Current state key
* @param bias Previous bias key
*/
ImuFactor2(Key state_i, Key state_j, Key bias,
const PreintegratedImuMeasurements& preintegratedMeasurements);
virtual ~ImuFactor2() {
}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const;
/// @name Testable
/// @{
GTSAM_EXPORT friend std::ostream& operator<<(std::ostream& os, const ImuFactor2&);
virtual void print(const std::string& s, const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const;
virtual bool equals(const NonlinearFactor& expected, double tol = 1e-9) const;
/// @}
/** Access the preintegrated measurements. */
const PreintegratedImuMeasurements& preintegratedMeasurements() const {
return _PIM_;
}
/** implement functions needed to derive from Factor */
/// vector of errors
Vector evaluateError(const NavState& state_i, const NavState& state_j,
const imuBias::ConstantBias& bias_i, //
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const;
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & boost::serialization::make_nvp("NoiseModelFactor3",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(_PIM_);
}
};
// class ImuFactor2
template <>
struct traits<PreintegratedImuMeasurements> : public Testable<PreintegratedImuMeasurements> {};
template <>
struct traits<ImuFactor> : public Testable<ImuFactor> {};
template <>
struct traits<ImuFactor2> : public Testable<ImuFactor2> {};
} /// namespace gtsam