osvr::kalman::OrientationConstantVelocityProcessModel Class Reference

A model for a 3DOF pose (with angular velocity) More...

#include <OrientationConstantVelocity.h>

Public Types
using	State = orient_externalized_rotation::State
using	StateVector = orient_externalized_rotation::StateVector
using	StateSquareMatrix = orient_externalized_rotation::StateSquareMatrix
using	NoiseAutocorrelation = types::Vector< 3 >

Public Member Functions
	OrientationConstantVelocityProcessModel (double orientationNoise=0.1)
void	setNoiseAutocorrelation (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
	This is Q(deltaT) - the Sampled Process Noise Covariance. More...
StateVector	computeEstimate (State &state, double dt) const
	Returns a 6-element vector containing a predicted state based on a constant velocity process model. More...

Detailed Description

A model for a 3DOF pose (with angular velocity)

Member Function Documentation

computeEstimate()

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

inline

Returns a 6-element vector containing a predicted state based on a constant velocity process model.

getSampledProcessNoiseCovariance()

StateSquareMatrix osvr::kalman::OrientationConstantVelocityProcessModel::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.

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

• inc/osvr/Kalman/OrientationConstantVelocity.h