Inheritance diagram for mrs_lib::LKF_MRS_odom:

Collaboration diagram for mrs_lib::LKF_MRS_odom:

Public Types
using	Base_class = LKF< 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	A_t = typename Base_class::A_t

using	B_t = typename Base_class::B_t

using	H_t = typename Base_class::H_t

using	Q_t = typename Base_class::Q_t

using	coeff_A_t = A_t

typedef Eigen::Matrix< unsigned, n, n >	dtexp_A_t

using	coeff_B_t = B_t

typedef Eigen::Matrix< unsigned, n, m >	dtexp_B_t

Public Types inherited from mrs_lib::LKF< 3, 1, 1 >
using	Base_class = KalmanFilter< n, m, p >
	Base class of this class.

using	x_t = typename Base_class::x_t
	State vector type $n \times 1$ .

using	u_t = typename Base_class::u_t
	Input vector type $m \times 1$ .

using	z_t = typename Base_class::z_t
	Measurement vector type $p \times 1$ .

using	P_t = typename Base_class::P_t
	State uncertainty covariance matrix type $n \times n$ .

using	R_t = typename Base_class::R_t
	Measurement noise covariance matrix type $p \times p$ .

using	Q_t = typename Base_class::Q_t
	Process noise covariance matrix type $n \times n$ .

using	statecov_t = typename Base_class::statecov_t
	Helper struct for passing around the state and its covariance in one variable.

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 $n \times p$ .

Public Types inherited from mrs_lib::KalmanFilter< 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
	LKF_MRS_odom (const std::vector< H_t > &Hs, const double default_dt=1)

virtual statecov_t	predict (const statecov_t &sc, const u_t &u, const Q_t &Q, double dt) const override

virtual statecov_t	correct (const statecov_t &sc, const z_t &z, const R_t &R, int param=0) const

x_t	state_predict_optimized (const x_t &x_prev, const u_t &u, double dt) const

P_t	covariance_predict_optimized (const P_t &P, const Q_t &Q, double dt) const

Public Member Functions inherited from mrs_lib::LKF< 3, 1, 1 >
	LKF ()
	Convenience default constructor. More...

	LKF (const A_t &A, const B_t &B, const H_t &H)
	The main constructor. 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...

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

Public Member Functions inherited from mrs_lib::KalmanFilter< 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 = 3

static const int	m = 1

static const int	p = 1

Static Public Attributes inherited from mrs_lib::LKF< 3, 1, 1 >
static constexpr int	n
	Length of the state vector of the system.

static constexpr int	m
	Length of the input vector of the system.

static constexpr int	p
	Length of the measurement vector of the system.

Static Public Attributes inherited from mrs_lib::KalmanFilter< 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.

Additional Inherited Members
Public Attributes inherited from mrs_lib::LKF< 3, 1, 1 >
A_t	A
	The system transition matrix $n \times n$ .

B_t	B
	The input to state mapping matrix $n \times m$ .

H_t	H
	The state to measurement mapping matrix $p \times n$ .

Protected Member Functions inherited from mrs_lib::LKF< 3, 1, 1 >
virtual K_t	computeKalmanGain (const statecov_t &sc,[[maybe_unused]] const z_t &z, const R_t &R, const H_t &H) 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 inherited from mrs_lib::LKF< 3, 1, 1 >
static P_t	covariance_predict (const A_t &A, const P_t &P, const Q_t &Q, const double dt)

static std::enable_if< check==0, x_t >::type	state_predict (const A_t &A, const x_t &x,[[maybe_unused]] const B_t &B,[[maybe_unused]] const u_t &u)

static std::enable_if< check !=0, x_t >::type	state_predict (const A_t &A, const x_t &x, const B_t &B, const u_t &u)

static R_t	invert_W (R_t W)

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

include/mrs_lib/lkf.h
src/lkf/LKF_MRS_odom.cpp

Public Types

Public Member Functions

Static Public Attributes

Additional Inherited Members