A basically-constant-velocity model, with the addition of some damping of the velocities inspired by TAG. More...

Public Types
using	State = pose_externalized_rotation::State

using	StateVector = pose_externalized_rotation::StateVector

using	StateSquareMatrix = pose_externalized_rotation::StateSquareMatrix

using	BaseProcess = PoseConstantVelocityProcessModel

using	NoiseAutocorrelation = BaseProcess::NoiseAutocorrelation

Public Member Functions
	PoseSeparatelyDampedConstantVelocityProcessModel (double positionDamping=0.3, double orientationDamping=0.01, double positionNoise=0.01, double orientationNoise=0.1)

void	setNoiseAutocorrelation (double positionNoise=0.01, double orientationNoise=0.1)

void	setNoiseAutocorrelation (NoiseAutocorrelation const &noise)

void	setDamping (double posDamping, double oriDamping)
	Set the damping - must be in (0, 1)

StateSquareMatrix	getStateTransitionMatrix (State const &, double dt) const
	Also known as the "process model jacobian" in TAG, this is A.

void	predictState (State &s, double dt)

StateSquareMatrix	getSampledProcessNoiseCovariance (double dt) const
	This is Q(deltaT) - the Sampled Process Noise Covariance. More...

StateVector	computeEstimate (State &state, double dt) const
	Returns a 12-element vector containing a predicted state based on a constant velocity process model. More...

Detailed Description

A basically-constant-velocity model, with the addition of some damping of the velocities inspired by TAG.

This model has separate damping/attenuation of linear and angular velocities.

Member Function Documentation

StateVector osvr::kalman::PoseSeparatelyDampedConstantVelocityProcessModel::computeEstimate	(	State &	state,
		double	dt
	)		const

inline

Returns a 12-element vector containing a predicted state based on a constant velocity process model.

StateSquareMatrix osvr::kalman::PoseSeparatelyDampedConstantVelocityProcessModel::getSampledProcessNoiseCovariance ( double dt ) const

inline

This is Q(deltaT) - the Sampled Process Noise Covariance.

Returns: a matrix of dimension n x n. Note that it is real symmetrical (self-adjoint), so .selfAdjointView<Eigen::Upper>() might provide useful performance enhancements.

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