Inheritance diagram for mrs_lib::JLKF< n_states, n_inputs, n_measurements, n_biases >:

Collaboration diagram for mrs_lib::JLKF< n_states, n_inputs, n_measurements, n_biases >:

Classes
struct	inverse_exception
	This exception is thrown when taking the inverse of a matrix fails. More...

Public Types
using	Base_class = KalmanFilterAloamGarm< n, m, p >

using	x_t = typename Base_class::x_t

using	u_t = typename Base_class::u_t

using	z_t = typename Base_class::z_t

using	P_t = typename Base_class::P_t

using	R_t = typename Base_class::R_t

using	statecov_t = typename Base_class::statecov_t

using	Q_t = typename Base_class::Q_t

typedef Eigen::Matrix< double, n, n >	A_t
	System transition matrix type $n \times n$ .

typedef Eigen::Matrix< double, n, m >	B_t
	Input to state mapping matrix type $n \times m$ .

typedef Eigen::Matrix< double, p, n >	H_t
	State to measurement mapping matrix type $p \times n$ .

typedef Eigen::Matrix< double, n, p >	K_t
	Kalman gain matrix type $n \times p$ .

typedef Eigen::Matrix< double, p, p >	C_t
	correntropy gain $p \times p$

typedef Eigen::Matrix< double, n, n >	D_t
	D $n \times n$ .

using	generateA_t = std::function< A_t(double)>

using	generateB_t = std::function< B_t(double)>

Public Types inherited from mrs_lib::KalmanFilterAloamGarm< n_states, n_inputs, n_measurements >
typedef Eigen::Matrix< double, n, 1 >	x_t
	State vector type $n \times 1$ .

typedef Eigen::Matrix< double, m, 1 >	u_t
	Input vector type $m \times 1$ .

typedef Eigen::Matrix< double, p, 1 >	z_t
	Measurement vector type $p \times 1$ .

typedef Eigen::Matrix< double, n, n >	P_t
	State uncertainty covariance matrix type $n \times n$ .

typedef Eigen::Matrix< double, p, p >	R_t
	Measurement noise covariance matrix type $p \times p$ .

typedef Eigen::Matrix< double, n, n >	Q_t
	Process noise covariance matrix type $n \times n$ .

Public Member Functions
	JLKF (const generateA_t &generateA, const generateB_t &generateB, const H_t &H, const ros::NodeHandle &nh, const double &nis_thr, const double &nis_avg_thr)
	The main constructor. More...

virtual statecov_t	predict (const statecov_t &sc, const u_t &u, const Q_t &Q, double dt) const override
	Applies the prediction (time) step of the Kalman filter. More...

virtual statecov_t	correct (const statecov_t &sc, const z_t &z, const R_t &R) const override
	Applies the correction (update, measurement, data) step of the Kalman filter. More...

Public Member Functions inherited from mrs_lib::KalmanFilterAloamGarm< n_states, n_inputs, n_measurements >
virtual statecov_t	correct (const statecov_t &sc, const z_t &z, const R_t &R) const =0
	Applies the correction (update, measurement, data) step of the Kalman filter. More...

virtual statecov_t	predict (const statecov_t &sc, const u_t &u, const Q_t &Q, double dt) const =0
	Applies the prediction (time) step of the Kalman filter. More...

Static Public Attributes
static const int	n = n_states

static const int	m = n_inputs

static const int	p = n_measurements

Static Public Attributes inherited from mrs_lib::KalmanFilterAloamGarm< n_states, n_inputs, n_measurements >
static const int	n = n_states
	Length of the state vector of the system.

static const int	m = n_inputs
	Length of the input vector of the system.

static const int	p = n_measurements
	Length of the measurement vector of the system.

Protected Member Functions
virtual K_t	computeKalmanGain (const statecov_t &sc, [[maybe_unused]] const z_t &z, const R_t &R, const H_t &H, double &nis, H_t &H_out, const double &nis_thr, const double &nis_avg_thr) const

statecov_t ::type	correction_impl (const statecov_t &sc, const z_t &z, const R_t &R, const H_t &H) const

Static Protected Member Functions
static R_t	invert_W (R_t W)

Constructor & Destructor Documentation

◆ JLKF()

template<int n_states, int n_inputs, int n_measurements, int n_biases>

mrs_lib::JLKF< n_states, n_inputs, n_measurements, n_biases >::JLKF	(	const generateA_t &	generateA,
		const generateB_t &	generateB,
		const H_t &	H,
		const ros::NodeHandle &	nh,
		const double &	nis_thr,
		const double &	nis_avg_thr
	)

inline

The main constructor.

Parameters

generateA	a function, which returns the state transition matrix `A` based on the time difference `dt`.
generateB	a function, which returns the input to state mapping matrix `B` based on the time difference `dt`.
H	the state to measurement mapping matrix.

Member Function Documentation

◆ correct()

template<int n_states, int n_inputs, int n_measurements, int n_biases>

virtual statecov_t mrs_lib::JLKF< n_states, n_inputs, n_measurements, n_biases >::correct	(	const statecov_t &	sc,
		const z_t &	z,
		const R_t &	R
	)		const

inlineoverridevirtual

Applies the correction (update, measurement, data) step of the Kalman filter.

This method applies the linear Kalman filter correction step to the state and covariance passed in sc using the measurement z and measurement noise R. The updated state and covariance after the correction step is returned.

Parameters

sc	The state and covariance to which the correction step is to be applied.
z	The measurement vector to be used for correction.
R	The measurement noise covariance matrix to be used for correction.

Returns: The state and covariance after the correction update.

◆ predict()

template<int n_states, int n_inputs, int n_measurements, int n_biases>

virtual statecov_t mrs_lib::JLKF< n_states, n_inputs, n_measurements, n_biases >::predict	(	const statecov_t &	sc,
		const u_t &	u,
		const Q_t &	Q,
		double	dt
	)		const

inlineoverridevirtual

Applies the prediction (time) step of the Kalman filter.

This method applies the linear Kalman filter prediction step to the state and covariance passed in sc using the input u and process noise Q. The process noise covariance Q is scaled by the dt parameter. The updated state and covariance after the prediction step is returned.

Parameters

sc	The state and covariance to which the prediction step is to be applied.
u	The input vector to be used for prediction.
Q	The process noise covariance matrix to be used for prediction.
dt	Used to scale the process noise covariance `Q` and to generate the state transition and input to state mapping matrices `A` and \B using the functions, passed in the object's constructor.

Returns: The state and covariance after the prediction step.

Note: Note that the dt parameter is used to scale the process noise covariance Q and to generate the system matrices #A or #B using the functions, passed in the constructor!

The documentation for this class was generated from the following file:

include/mrs_lib/jlkf.h

Classes

Public Types

Public Member Functions

Static Public Attributes

Protected Member Functions

Static Protected Member Functions

Constructor & Destructor Documentation

◆ JLKF()

Member Function Documentation

◆ correct()

◆ predict()