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. More...

#include <pcl/sample_consensus/mlesac.h>

Inheritance diagram for pcl::MaximumLikelihoodSampleConsensus< PointT >:

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Collaboration diagram for pcl::MaximumLikelihoodSampleConsensus< PointT >:

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Public Types
typedef boost::shared_ptr < SampleConsensus >	Ptr

typedef boost::shared_ptr < const SampleConsensus >	ConstPtr

Public Member Functions
	MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model)
	MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor. More...

	MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model, double threshold)
	MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor. More...

bool	computeModel (int debug_verbosity_level=0)
	Compute the actual model and find the inliers. More...

void	setEMIterations (int iterations)
	Set the number of EM iterations. More...

int	getEMIterations () const
	Get the number of EM iterations. More...

void	setDistanceThreshold (double threshold)
	Set the distance to model threshold. More...

double	getDistanceThreshold ()
	Get the distance to model threshold, as set by the user. More...

void	setMaxIterations (int max_iterations)
	Set the maximum number of iterations. More...

int	getMaxIterations ()
	Get the maximum number of iterations, as set by the user. More...

void	setProbability (double probability)
	Set the desired probability of choosing at least one sample free from outliers. More...

double	getProbability ()
	Obtain the probability of choosing at least one sample free from outliers, as set by the user. More...

void	getRandomSamples (const boost::shared_ptr< std::vector< int > > &indices, size_t nr_samples, std::set< int > &indices_subset)
	Get a set of randomly selected indices. More...

void	getModel (std::vector< int > &model)
	Return the best model found so far. More...

void	getInliers (std::vector< int > &inliers)
	Return the best set of inliers found so far for this model. More...

void	getModelCoefficients (Eigen::VectorXf &model_coefficients)
	Return the model coefficients of the best model found so far. More...

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

typedef boost::shared_ptr<const SampleConsensus> pcl::SampleConsensus< PointT >::ConstPtr

inherited

Definition at line 65 of file sac.h.

typedef boost::shared_ptr<SampleConsensus> pcl::SampleConsensus< PointT >::Ptr

inherited

Definition at line 61 of file sac.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 >

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.

double pcl::SampleConsensus< PointT >::getDistanceThreshold ( )

inlineinherited

Get the distance to model threshold, as set by the user.

Definition at line 125 of file sac.h.

template<typename PointT >

int pcl::MaximumLikelihoodSampleConsensus< PointT >::getEMIterations ( ) const

inline

Get the number of EM iterations.

Definition at line 107 of file mlesac.h.

void pcl::SampleConsensus< PointT >::getInliers ( std::vector< int > & inliers )

inlineinherited

Return the best set of inliers found so far for this model.

Parameters

[out] inliers the resultant set of inliers

Definition at line 178 of file sac.h.

int pcl::SampleConsensus< PointT >::getMaxIterations ( )

inlineinherited

Get the maximum number of iterations, as set by the user.

Definition at line 135 of file sac.h.

void pcl::SampleConsensus< PointT >::getModel ( std::vector< int > & model )

inlineinherited

Return the best model found so far.

Parameters

[out] model the resultant model

Definition at line 172 of file sac.h.

void pcl::SampleConsensus< PointT >::getModelCoefficients ( Eigen::VectorXf & model_coefficients )

inlineinherited

Return the model coefficients of the best model found so far.

Parameters

[out] model_coefficients the resultant model coefficients

Definition at line 184 of file sac.h.

double pcl::SampleConsensus< PointT >::getProbability ( )

inlineinherited

Obtain the probability of choosing at least one sample free from outliers, as set by the user.

Definition at line 146 of file sac.h.

void pcl::SampleConsensus< PointT >::getRandomSamples	(	const boost::shared_ptr< std::vector< int > > &	indices,
		size_t	nr_samples,
		std::set< int > &	indices_subset
	)

inlineinherited

Get a set of randomly selected indices.

Parameters

[in]	indices	the input indices vector
[in]	nr_samples	the desired number of point indices to randomly select
[out]	indices_subset	the resultant output set of randomly selected indices

Definition at line 158 of file sac.h.

void pcl::SampleConsensus< PointT >::setDistanceThreshold ( double threshold )

inlineinherited

Set the distance to model threshold.

Parameters

[in] threshold distance to model threshold

Definition at line 121 of file sac.h.

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.

void pcl::SampleConsensus< PointT >::setMaxIterations ( int max_iterations )

inlineinherited

Set the maximum number of iterations.

Parameters

[in] max_iterations maximum number of iterations

Definition at line 131 of file sac.h.

void pcl::SampleConsensus< PointT >::setProbability ( double probability )

inlineinherited

Set the desired probability of choosing at least one sample free from outliers.

Parameters

[in] probability the desired probability of choosing at least one sample free from outliers

Note: internally, the probability is set to 99% (0.99) by default.

Definition at line 142 of file sac.h.

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

/builddir/build/BUILD/PCL-1.6.0-Source/sample_consensus/include/pcl/sample_consensus/mlesac.h
/builddir/build/BUILD/PCL-1.6.0-Source/sample_consensus/include/pcl/sample_consensus/impl/mlesac.hpp

Public Types

Public Member Functions

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

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