#include <osvr/Kalman/PoseSeparatelyDampedConstantVelocity.h>

Public Member Functions
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.

StateSquareMatrix	getSampledProcessNoiseCovariance (double dt) const

StateVector	computeEstimate (State &state, double dt) const

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.

Definition at line 43 of file PoseSeparatelyDampedConstantVelocity.h.

Member Function Documentation

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.

Definition at line 96 of file PoseSeparatelyDampedConstantVelocity.h.

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.

Definition at line 102 of file PoseSeparatelyDampedConstantVelocity.h.

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

osvr/Kalman/PoseSeparatelyDampedConstantVelocity.h

Public Member Functions

Detailed Description

Member Function Documentation