Functions
IndexedVectorArray	imuAttitudeTrajectoryReconstruction (const IndexedVectorArray &y, const IndexedVectorArray &u, const Vector &xh0, const Matrix &p, const Matrix &q, const Matrix &r, double dt, bool withGyroBias)

IndexedVectorArray	imuAttitudeTrajectoryReconstruction (const IndexedVectorArray &y, const Vector &xh0, const Matrix &p, const Matrix &q, const Matrix &r, double dt, bool withGyroBias)

IndexedVectorArray	imuMultiplicativeAttitudeReconstruction (const IndexedVectorArray &y, const IndexedVectorArray &u, const Vector &xh0, const Matrix &p, const Matrix &q, const Matrix &r, double dt)
	Provides the estimation of the state (mostly attitude) of an IMU, given the measurements of the IMU and the input (which provides the acceleration/jerk). This method uses multiplicative extended Kalman filtering, we need to provide it with an initial guess, a covariance matrix of this initial guess and covariance matrices of the state perturbations and measurement noises. More...

IndexedVectorArray	imuMultiplicativeAttitudeReconstruction (const IndexedVectorArray &y, const Vector &xh0, const Matrix &p, const Matrix &q, const Matrix &r, double dt)
	Provides the estimation of the state (mostly attitude) of an IMU, given the measurements of the IMU without knowing the input. The input is assumed to be zero over the observation. This method uses multiplicative extended Kalman filtering, we need to provide it with an initial guess, a covariance matrix of this initial guess and covariance matrices of the state perturbations and measurement noises. More...

stateObservation::IndexedVectorArray	offlineEKFFlexibilityEstimation (const stateObservation::IndexedVectorArray &y, const stateObservation::IndexedVectorArray &u, const Vector &xh0, unsigned numberOfContacts, const std::vector< Vector3, Eigen::aligned_allocator< Vector3 >> &contactsPositions, double dt, IndexedVectorArray ino=0x0, IndexedVectorArray premea=0x0)

stateObservation::IndexedVectorArray	offlineEKFFlexibilityEstimation (const stateObservation::IndexedVectorArray &y, const Vector &xh0, unsigned numberOfContacts, const std::vector< Vector3, Eigen::aligned_allocator< Vector3 >> &contactsPositions, double dt)

stateObservation::IndexedVectorArray	offlineModelBaseFlexEstimation (const stateObservation::IndexedVectorArray &y, const stateObservation::IndexedVectorArray &u, const Matrix &xh0, const stateObservation::IndexedVectorArray numberOfContacts, double dt, double mass, bool withForce, const stateObservation::IndexedMatrixArray &Q=stateObservation::IndexedMatrixArray(), const stateObservation::IndexedMatrixArray &R=stateObservation::IndexedMatrixArray(), const Matrix3 &kfe=Matrix3::Zero(), const Matrix3 &kfv=Matrix3::Zero(), const Matrix3 &kte=Matrix3::Zero(), const Matrix3 &ktv=Matrix3::Zero(), IndexedVectorArray prediction=0x0, IndexedVectorArray inovation=0x0, IndexedVectorArray predictedMeasurements=0x0, IndexedVectorArray simulatedMeasurements=0x0, int verbose=0x0)

Function Documentation

◆ imuAttitudeTrajectoryReconstruction() [1/2]

IndexedVectorArray stateObservation::examples::imuAttitudeTrajectoryReconstruction	(	const IndexedVectorArray &	y,
		const IndexedVectorArray &	u,
		const Vector &	xh0,
		const Matrix &	p,
		const Matrix &	q,
		const Matrix &	r,
		double	dt,
		bool	withGyroBias
	)

◆ imuAttitudeTrajectoryReconstruction() [2/2]

IndexedVectorArray stateObservation::examples::imuAttitudeTrajectoryReconstruction	(	const IndexedVectorArray &	y,
		const Vector &	xh0,
		const Matrix &	p,
		const Matrix &	q,
		const Matrix &	r,
		double	dt,
		bool	withGyroBias
	)

◆ imuMultiplicativeAttitudeReconstruction() [1/2]

IndexedVectorArray stateObservation::examples::imuMultiplicativeAttitudeReconstruction	(	const IndexedVectorArray &	y,
		const IndexedVectorArray &	u,
		const Vector &	xh0,
		const Matrix &	p,
		const Matrix &	q,
		const Matrix &	r,
		double	dt
	)

Provides the estimation of the state (mostly attitude) of an IMU, given the measurements of the IMU and the input (which provides the acceleration/jerk). This method uses multiplicative extended Kalman filtering, we need to provide it with an initial guess, a covariance matrix of this initial guess and covariance matrices of the state perturbations and measurement noises.

Parameters

y	IMU measurements
u	the inputs of the dynamical system
xh0	an initial guess of the state
p	the covariance matrix of the error of the initial guess
q	the covariance matrix of the process noise (state perturbation)
r	the covariance matrix of the measurement noise
dt	the time discretization period

◆ imuMultiplicativeAttitudeReconstruction() [2/2]

IndexedVectorArray stateObservation::examples::imuMultiplicativeAttitudeReconstruction	(	const IndexedVectorArray &	y,
		const Vector &	xh0,
		const Matrix &	p,
		const Matrix &	q,
		const Matrix &	r,
		double	dt
	)

Provides the estimation of the state (mostly attitude) of an IMU, given the measurements of the IMU without knowing the input. The input is assumed to be zero over the observation. This method uses multiplicative extended Kalman filtering, we need to provide it with an initial guess, a covariance matrix of this initial guess and covariance matrices of the state perturbations and measurement noises.

Parameters

y	IMU measurements
xh0	an initial guess of the state
p	the covariance matrix of the error of the initial guess
q	the covariance matrix of the process noise (state perturbation)
r	the covariance matrix of the measurement noise
dt	the time discretization period

◆ offlineEKFFlexibilityEstimation() [1/2]

stateObservation::IndexedVectorArray stateObservation::examples::offlineEKFFlexibilityEstimation	(	const stateObservation::IndexedVectorArray &	y,
		const stateObservation::IndexedVectorArray &	u,
		const Vector &	xh0,
		unsigned	numberOfContacts,
		const std::vector< Vector3, Eigen::aligned_allocator< Vector3 >> &	contactsPositions,
		double	dt,
		IndexedVectorArray *	ino = `0x0`,
		IndexedVectorArray *	premea = `0x0`
	)

◆ offlineEKFFlexibilityEstimation() [2/2]

stateObservation::IndexedVectorArray stateObservation::examples::offlineEKFFlexibilityEstimation	(	const stateObservation::IndexedVectorArray &	y,
		const Vector &	xh0,
		unsigned	numberOfContacts,
		const std::vector< Vector3, Eigen::aligned_allocator< Vector3 >> &	contactsPositions,
		double	dt
	)

◆ offlineModelBaseFlexEstimation()

stateObservation::IndexedVectorArray stateObservation::examples::offlineModelBaseFlexEstimation	(	const stateObservation::IndexedVectorArray &	y,
		const stateObservation::IndexedVectorArray &	u,
		const Matrix &	xh0,
		const stateObservation::IndexedVectorArray	numberOfContacts,
		double	dt,
		double	mass,
		bool	withForce,
		const stateObservation::IndexedMatrixArray &	Q = `stateObservation::IndexedMatrixArray()`,
		const stateObservation::IndexedMatrixArray &	R = `stateObservation::IndexedMatrixArray()`,
		const Matrix3 &	kfe = `Matrix3::Zero()`,
		const Matrix3 &	kfv = `Matrix3::Zero()`,
		const Matrix3 &	kte = `Matrix3::Zero()`,
		const Matrix3 &	ktv = `Matrix3::Zero()`,
		IndexedVectorArray *	prediction = `0x0`,
		IndexedVectorArray *	inovation = `0x0`,
		IndexedVectorArray *	predictedMeasurements = `0x0`,
		IndexedVectorArray *	simulatedMeasurements = `0x0`,
		int	verbose = `0x0`
	)

Functions