osvr::kalman::PoseConstantVelocityProcessModel Class Reference

A constant-velocity model for a 6DOF pose (with velocities) More...

#include <osvr/Kalman/PoseConstantVelocity.h>

Public Types
using	State = pose_externalized_rotation::State
using	StateVector = pose_externalized_rotation::StateVector
using	StateSquareMatrix = pose_externalized_rotation::StateSquareMatrix
using	NoiseAutocorrelation = types::Vector< 6 >

Public Member Functions
	PoseConstantVelocityProcessModel (double positionNoise=0.01, double orientationNoise=0.1)
void	setNoiseAutocorrelation (double positionNoise=0.01, double orientationNoise=0.1)
void	setNoiseAutocorrelation (NoiseAutocorrelation const &noise)
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
StateVector	computeEstimate (State &state, double dt) const

Detailed Description

A constant-velocity model for a 6DOF pose (with velocities)

Definition at line 40 of file PoseConstantVelocity.h.

Member Function Documentation

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

inline

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

Returns

a matrix of dimension n x n.

Like all covariance matrices, it is real symmetrical (self-adjoint), so .selfAdjointView<Eigen::Upper>() might provide useful performance enhancements in some algorithms.

Definition at line 79 of file PoseConstantVelocity.h.

StateVector osvr::kalman::PoseConstantVelocityProcessModel::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 99 of file PoseConstantVelocity.h.

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

• osvr/Kalman/PoseConstantVelocity.h