clang-formatted, changed getFoobar() to foobar()
scottiyio
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@ -13,7 +13,8 @@
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* @file LIEKF.h
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* @brief Base and classes for Left Invariant Extended Kalman Filters
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*
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* Templates are implemented for a Left Invariant Extended Kalman Filter operating on Lie Groups.
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* Templates are implemented for a Left Invariant Extended Kalman Filter
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* operating on Lie Groups.
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*
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*
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* @date April 24, 2025
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@ -22,59 +23,66 @@
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*/
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#pragma once
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#include <functional>
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Vector.h>
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#include <gtsam/base/OptionalJacobian.h>
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#include <gtsam/base/Vector.h>
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#include <Eigen/Dense>
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#include <functional>
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namespace gtsam {
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/**
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* @brief Base class for Left Invariant Extended Kalman Filter (LIEKF)
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*
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* This class provides the prediction and update structure based on control inputs and a measurement function.
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*
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* @tparam LieGroup Lie group used for state representation (e.g., Pose2, Pose3, NavState)
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* @tparam Measurement Type of measurement (e.g. Vector3 for a GPS measurement for 3D position)
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*/
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* @brief Base class for Left Invariant Extended Kalman Filter (LIEKF)
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*
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* This class provides the prediction and update structure based on control
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* inputs and a measurement function.
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*
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* @tparam LieGroup Lie group used for state representation (e.g., Pose2,
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* Pose3, NavState)
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* @tparam Measurement Type of measurement (e.g. Vector3 for a GPS measurement
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* for 3D position)
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*/
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template <typename LieGroup, typename Measurement>
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class LIEKF {
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public:
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static constexpr int n =
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traits<LieGroup>::dimension; ///< Dimension of the state.
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static constexpr int m =
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traits<Measurement>::dimension; ///< Dimension of the measurement.
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public:
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static constexpr int n = traits<LieGroup>::dimension; ///< Dimension of the state.
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static constexpr int m = traits<Measurement>::dimension; ///< Dimension of the measurement.
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using MeasurementFunction = std::function<Measurement(const LieGroup&, OptionalJacobian<m, n>)>; ///< Typedef for the measurement function.
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using MatrixN = Eigen::Matrix<double, n, n>; ///< Typedef for the identity matrix.
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using MeasurementFunction = std::function<Measurement(
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const LieGroup&,
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OptionalJacobian<m, n>)>; ///< Typedef for the measurement function.
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using MatrixN =
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Eigen::Matrix<double, n, n>; ///< Typedef for the identity matrix.
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/**
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* @brief Construct with a measurement function
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* @param X0 Initial State
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* @param P0 Initial Covariance
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* @param h Measurement function
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*/
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LIEKF(const LieGroup& X0, const Matrix& P0, MeasurementFunction& h) // X_ P_
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* @brief Construct with a measurement function
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* @param X0 Initial State
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* @param P0 Initial Covariance
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* @param h Measurement function
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*/
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LIEKF(const LieGroup& X0, const Matrix& P0, MeasurementFunction& h) // X_ P_
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: X(X0), P(P0), h_(h) {}
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/**
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* @brief Get current state estimate.
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* @return Const reference to the state estiamte.
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*/
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const LieGroup& state() const { return X; }
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/**
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* @brief Get current state estimate.
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* @return Const reference to the state estiamte.
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*/
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const LieGroup& getState() const { return X; }
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* @brief Get current covariance estimate.
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* @return Const reference to the covariance estimate.
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*/
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const Matrix& covariance() const { return P; }
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/**
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* @brief Get current covariance estimate.
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* @return Const reference to the covariance estimate.
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*/
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const Matrix& getCovariance() const { return P; }
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/**
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* @brief Prediction stage with a Lie group element U.
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* @param U Lie group control input
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* @param Q Process noise covariance matrix.
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*/
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* @brief Prediction stage with a Lie group element U.
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* @param U Lie group control input
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* @param Q Process noise covariance matrix.
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*/
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void predict(const LieGroup& U, const Matrix& Q) {
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LieGroup::Jacobian A;
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X = X.compose(U, A);
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@ -82,83 +90,92 @@ public:
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}
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/**
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* @brief Prediction stage with a control vector u and a time interval dt.
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* @param u Control vector element
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* @param dt Time interval
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* @param Q Process noise covariance matrix.
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*/
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void predict(const Vector& u, double dt, const Matrix& Q) {
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predict(LieGroup::Expmap(u*dt), Q);d
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* @brief Prediction stage with a control vector u and a time interval dt.
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* @param u Control vector element
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* @param dt Time interval
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* @param Q Process noise covariance matrix.
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*/
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void predict(const Vector& u, double dt, const Matrix& Q) {
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predict(LieGroup::Expmap(u * dt), Q);
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}
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/**
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* @brief Update stage using a measurement and measurement covariance.
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* @param z Measurement
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* @param R Measurement noise covariance matrix.
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*/
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* @brief Update stage using a measurement and measurement covariance.
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* @param z Measurement
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* @param R Measurement noise covariance matrix.
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*/
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void update(const Measurement& z, const Matrix& R) {
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Matrix H(m, n);
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Vector y = h_(X, H)-z;
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Vector y = h_(X, H) - z;
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Matrix S = H * P * H.transpose() + R;
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Matrix K = P * H.transpose() * S.inverse();
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X = X.expmap(-K * y);
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P = (I_n - K * H) * P; // move Identity to be a constant.
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P = (I_n - K * H) * P; // move Identity to be a constant.
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}
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protected:
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LieGroup X; ///< Current state estimate.
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Matrix P; ///< Current covariance estimate.
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private:
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MeasurementFunction h_; ///< Measurement function
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static const MatrixN I_n; ///< A nxn identity matrix used in the update stage of the LIEKF.
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protected:
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LieGroup X; ///< Current state estimate.
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Matrix P; ///< Current covariance estimate.
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private:
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MeasurementFunction h_; ///< Measurement function
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static const MatrixN
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I_n; ///< A nxn identity matrix used in the update stage of the LIEKF.
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};
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/**
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* @brief Create the static identity matrix I_n of size nxn for use in the update stage.
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* @tparam LieGroup Lie group used for state representation (e.g., Pose2, Pose3, NavState)
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* @tparam Measurement Type of measurement (e.g. Vector3 for a GPS measurement for 3D position)
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*/
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* @brief Create the static identity matrix I_n of size nxn for use in the
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* update stage.
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* @tparam LieGroup Lie group used for state representation (e.g., Pose2,
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* Pose3, NavState)
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* @tparam Measurement Type of measurement (e.g. Vector3 for a GPS measurement
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* for 3D position)
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*/
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template <typename LieGroup, typename Measurement>
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const typename LIEKF<LieGroup, Measurement>::MatrixN LIEKF<LieGroup, Measurement>::I_n
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= typename LIEKF<LieGroup, Measurement>::MatrixN::Identity();
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const typename LIEKF<LieGroup, Measurement>::MatrixN
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LIEKF<LieGroup, Measurement>::I_n =
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typename LIEKF<LieGroup, Measurement>::MatrixN::Identity();
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/**
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* @brief General Left Invariant Extended Kalman Filter with dynamics function.
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*
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* This class extends the LIEKF class to include a dynamics function f. The dynamics maps
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* a state and control vector to a tangent vector xi.
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* This class extends the LIEKF class to include a dynamics function f. The
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* dynamics maps a state and control vector to a tangent vector xi.
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*
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* @tparam LieGroup The Lie group type for the state.
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* @tparam Measurement The type of the measurement.
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* @tparam _p The dimension of the control vector.
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*/
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template <typename LieGroup, typename Measurement, size_t _p>
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class GeneralLIEKF:public LIEKF<LieGroup, Measurement> {
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class GeneralLIEKF : public LIEKF<LieGroup, Measurement> {
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public:
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using Control = Eigen::Matrix<double, _p, 1>; ///< Typedef for the control vector.
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using TangentVector = typename traits<LieGroup>::TangentVector; ///< Typedef for the tangent vector.
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using Dynamics = std::function<TangentVector(const LieGroup&, const Control&, ///< Typedef for the dynamics function.
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OptionalJacobian<n, n>)>;
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using Control =
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Eigen::Matrix<double, _p, 1>; ///< Typedef for the control vector.
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using TangentVector =
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typename traits<LieGroup>::TangentVector; ///< Typedef for the tangent
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///< vector.
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using Dynamics = std::function<TangentVector(
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const LieGroup&, const Control&, ///< Typedef for the dynamics function.
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OptionalJacobian<n, n>)>;
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/**
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* @brief Construct with general dynamics
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* @param X0 Initial State
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* @param P0 Initial Covariance
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* @param f Dynamics function that depends on state and control vector
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* @param h Measurement function
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*/
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GeneralLIEKF(const LieGroup& X0, const Matrix& P0, Dynamics& f, MeasurementFunction&
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h) : LIEKF(X0, P0, h), f_(f) {}
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* @brief Construct with general dynamics
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* @param X0 Initial State
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* @param P0 Initial Covariance
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* @param f Dynamics function that depends on state and control vector
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* @param h Measurement function
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*/
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GeneralLIEKF(const LieGroup& X0, const Matrix& P0, Dynamics& f,
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MeasurementFunction& h)
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: LIEKF(X0, P0, h), f_(f) {}
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/**
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* @brief Prediction stage with a dynamics function that calculates the tangent vector xi in the tangent space.
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* @param u Control vector element
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* @param dt Time interval
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* @param Q Process noise covariance matrix.
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*/
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* @brief Prediction stage with a dynamics function that calculates the
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* tangent vector xi in the tangent space.
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* @param u Control vector element
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* @param dt Time interval
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* @param Q Process noise covariance matrix.
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*/
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void predict(const Control& u, double dt, const Matrix& Q) {
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LieGroup::Jacobian H;
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const TangentVector xi = f_(X, u, H);
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@ -167,10 +184,9 @@ h) : LIEKF(X0, P0, h), f_(f) {}
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X = X.compose(U);
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P = A * P * A.transpose() + Q;
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}
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private:
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Dynamics f_; ///< Dynamics function.
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private:
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Dynamics f_; ///< Dynamics function.
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};
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}// ends namespace
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} // namespace gtsam
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