MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000. More...

#include <mlesac.h>

Inheritance diagram for pcl::MaximumLikelihoodSampleConsensus< PointT >:

[legend]

List of all members.

Public Member Functions
bool	computeModel (int debug_verbosity_level=0)
	Compute the actual model and find the inliers.
int	getEMIterations () const
	Get the number of EM iterations.
	MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model)
	MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.
	MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model, double threshold)
	MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.
void	setEMIterations (int iterations)
	Set the number of EM iterations.
Protected Member Functions
void	computeMedian (const PointCloudConstPtr &cloud, const boost::shared_ptr< std::vector< int > > &indices, Eigen::Vector4f &median)
	Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32.
double	computeMedianAbsoluteDeviation (const PointCloudConstPtr &cloud, const boost::shared_ptr< std::vector< int > > &indices, double sigma)
	Compute the median absolute deviation: $MAD = \sigma * median_i (\| Xi - median_j(Xj) \|)$ .
void	getMinMax (const PointCloudConstPtr &cloud, const boost::shared_ptr< std::vector< int > > &indices, Eigen::Vector4f &min_p, Eigen::Vector4f &max_p)
	Determine the minimum and maximum 3D bounding box coordinates for a given set of points.
Private Types
typedef SampleConsensusModel < PointT >::PointCloudConstPtr	PointCloudConstPtr
typedef SampleConsensusModel < PointT >::Ptr	SampleConsensusModelPtr
Private Attributes
int	iterations_EM_
	Maximum number of EM (Expectation Maximization) iterations.
double	sigma_
	The MLESAC sigma parameter.

Detailed Description

template<typename PointT>
class pcl::MaximumLikelihoodSampleConsensus< PointT >

MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000.

Note:: MLESAC is useful in situations where most of the data samples belong to the model, and a fast outlier rejection algorithm is needed.

Author:: Radu Bogdan Rusu

Definition at line 55 of file mlesac.h.

Member Typedef Documentation

template<typename PointT>

typedef SampleConsensusModel<PointT>::PointCloudConstPtr pcl::MaximumLikelihoodSampleConsensus< PointT >::PointCloudConstPtr [private]

Definition at line 67 of file mlesac.h.

template<typename PointT>

typedef SampleConsensusModel<PointT>::Ptr pcl::MaximumLikelihoodSampleConsensus< PointT >::SampleConsensusModelPtr [private]

Reimplemented from pcl::SampleConsensus< PointT >.

Definition at line 66 of file mlesac.h.

Constructor & Destructor Documentation

template<typename PointT>

pcl::MaximumLikelihoodSampleConsensus< PointT >::MaximumLikelihoodSampleConsensus ( const SampleConsensusModelPtr & model ) [inline]

MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.

Parameters:

[in] model a Sample Consensus model

Definition at line 73 of file mlesac.h.

template<typename PointT>

pcl::MaximumLikelihoodSampleConsensus< PointT >::MaximumLikelihoodSampleConsensus	(	const SampleConsensusModelPtr &	model,
		double	threshold
	)		`[inline]`

MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.

Parameters:

[in]	model	a Sample Consensus model
[in]	threshold	distance to model threshold

Definition at line 85 of file mlesac.h.

Member Function Documentation

template<typename PointT >

void pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedian	(	const PointCloudConstPtr &	cloud,
		const boost::shared_ptr< std::vector< int > > &	indices,
		Eigen::Vector4f &	median
	)		`[protected]`

Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32.

Parameters:

[in]	cloud	the point cloud data message
[in]	indices	the point indices
[out]	median	the resultant median value

Definition at line 253 of file mlesac.hpp.

template<typename PointT >

double pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedianAbsoluteDeviation	(	const PointCloudConstPtr &	cloud,
		const boost::shared_ptr< std::vector< int > > &	indices,
		double	sigma
	)		`[protected]`

Compute the median absolute deviation:

$MAD = \sigma * median_i (| Xi - median_j(Xj) |)$

.

Note:: Sigma needs to be chosen carefully (a good starting sigma value is 1.4826)

Parameters:

[in]	cloud	the point cloud data message
[in]	indices	the set of point indices to use
[in]	sigma	the sigma value

Definition at line 196 of file mlesac.hpp.

template<typename PointT >

bool pcl::MaximumLikelihoodSampleConsensus< PointT >::computeModel ( int debug_verbosity_level = 0 ) [virtual]

Compute the actual model and find the inliers.

Parameters:

[in] debug_verbosity_level enable/disable on-screen debug information and set the verbosity level

Implements pcl::SampleConsensus< PointT >.

Definition at line 45 of file mlesac.hpp.

template<typename PointT>

int pcl::MaximumLikelihoodSampleConsensus< PointT >::getEMIterations ( ) const [inline]

Get the number of EM iterations.

Definition at line 107 of file mlesac.h.

template<typename PointT >

void pcl::MaximumLikelihoodSampleConsensus< PointT >::getMinMax	(	const PointCloudConstPtr &	cloud,
		const boost::shared_ptr< std::vector< int > > &	indices,
		Eigen::Vector4f &	min_p,
		Eigen::Vector4f &	max_p
	)		`[protected]`

Determine the minimum and maximum 3D bounding box coordinates for a given set of points.

Parameters:

[in]	cloud	the point cloud message
[in]	indices	the set of point indices to use
[out]	min_p	the resultant minimum bounding box coordinates
[out]	max_p	the resultant maximum bounding box coordinates

Definition at line 229 of file mlesac.hpp.

template<typename PointT>

void pcl::MaximumLikelihoodSampleConsensus< PointT >::setEMIterations ( int iterations ) [inline]

Set the number of EM iterations.

Parameters:

[in] iterations the number of EM iterations

Definition at line 103 of file mlesac.h.

Member Data Documentation

template<typename PointT>

int pcl::MaximumLikelihoodSampleConsensus< PointT >::iterations_EM_ [private]

Maximum number of EM (Expectation Maximization) iterations.

Definition at line 149 of file mlesac.h.

template<typename PointT>

double pcl::MaximumLikelihoodSampleConsensus< PointT >::sigma_ [private]

The MLESAC sigma parameter.

Definition at line 151 of file mlesac.h.

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

Public Member Functions

Protected Member Functions

Private Types

Private Attributes

Detailed Description

template<typename PointT> class pcl::MaximumLikelihoodSampleConsensus< PointT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename PointT>
class pcl::MaximumLikelihoodSampleConsensus< PointT >