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 >:

Collaboration diagram for pcl::MaximumLikelihoodSampleConsensus< PointT >:

Public Types
using	Ptr = shared_ptr< MaximumLikelihoodSampleConsensus< PointT > >

using	ConstPtr = shared_ptr< const MaximumLikelihoodSampleConsensus< PointT > >

Public Types inherited from pcl::SampleConsensus< PointT >
using	Ptr = shared_ptr< SampleConsensus< PointT > >

using	ConstPtr = shared_ptr< const SampleConsensus< PointT > >

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) override
	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...

Public Member Functions inherited from pcl::SampleConsensus< PointT >
	SampleConsensus (const SampleConsensusModelPtr &model, bool random=false)
	Constructor for base SAC. More...

	SampleConsensus (const SampleConsensusModelPtr &model, double threshold, bool random=false)
	Constructor for base SAC. More...

void	setSampleConsensusModel (const SampleConsensusModelPtr &model)
	Set the Sample Consensus model to use. More...

SampleConsensusModelPtr	getSampleConsensusModel () const
	Get the Sample Consensus model used. More...

virtual	~SampleConsensus ()=default
	Destructor for base SAC. More...

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

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

void	setNumberOfThreads (const int nr_threads=-1)
	Set the number of threads to use or turn off parallelization. More...

int	getNumberOfThreads () const
	Get the number of threads, as set by the user. More...

virtual bool	refineModel (const double sigma=3.0, const unsigned int max_iterations=1000)
	Refine the model found. More...

void	getRandomSamples (const IndicesPtr &indices, std::size_t nr_samples, std::set< index_t > &indices_subset)
	Get a set of randomly selected indices. More...

void	getModel (Indices &model) const
	Return the best model found so far. More...

void	getInliers (Indices &inliers) const
	Return the best set of inliers found so far for this model. More...

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

Protected Member Functions
double	computeMedianAbsoluteDeviation (const PointCloudConstPtr &cloud, const IndicesPtr &indices, double sigma) const
	Compute the median absolute deviation: More...

void	getMinMax (const PointCloudConstPtr &cloud, const IndicesPtr &indices, Eigen::Vector4f &min_p, Eigen::Vector4f &max_p) const
	Determine the minimum and maximum 3D bounding box coordinates for a given set of points. More...

void	computeMedian (const PointCloudConstPtr &cloud, const IndicesPtr &indices, Eigen::Vector4f &median) const
	Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32. More...

Protected Member Functions inherited from pcl::SampleConsensus< PointT >
double	rnd ()
	Boost-based random number generator. More...

Additional Inherited Members
Protected Attributes inherited from pcl::SampleConsensus< PointT >
SampleConsensusModelPtr	sac_model_
	The underlying data model used (i.e. More...

Indices	model_
	The model found after the last computeModel () as point cloud indices. More...

Indices	inliers_
	The indices of the points that were chosen as inliers after the last computeModel () call. More...

Eigen::VectorXf	model_coefficients_
	The coefficients of our model computed directly from the model found. More...

double	probability_
	Desired probability of choosing at least one sample free from outliers. More...

int	iterations_
	Total number of internal loop iterations that we've done so far. More...

double	threshold_
	Distance to model threshold. More...

int	max_iterations_
	Maximum number of iterations before giving up. More...

int	threads_
	The number of threads the scheduler should use, or a negative number if no parallelization is wanted. More...

boost::mt19937	rng_alg_
	Boost-based random number generator algorithm. More...

std::shared_ptr< boost::uniform_01< boost::mt19937 > >	rng_
	Boost-based random number generator distribution. 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 B. Rusu

Definition at line 57 of file mlesac.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointT >

using pcl::MaximumLikelihoodSampleConsensus< PointT >::ConstPtr = shared_ptr<const MaximumLikelihoodSampleConsensus<PointT> >

Definition at line 64 of file mlesac.h.

◆ Ptr

template<typename PointT >

using pcl::MaximumLikelihoodSampleConsensus< PointT >::Ptr = shared_ptr<MaximumLikelihoodSampleConsensus<PointT> >

Definition at line 63 of file mlesac.h.

Constructor & Destructor Documentation

◆ MaximumLikelihoodSampleConsensus() [1/2]

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 78 of file mlesac.h.

References pcl::SampleConsensus< PointT >::max_iterations_.

◆ MaximumLikelihoodSampleConsensus() [2/2]

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 90 of file mlesac.h.

References pcl::SampleConsensus< PointT >::max_iterations_.

Member Function Documentation

◆ computeMedian()

template<typename PointT >

void pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedian	(	const PointCloudConstPtr &	cloud,
		const IndicesPtr &	indices,
		Eigen::Vector4f &	median
	)		const

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 261 of file mlesac.hpp.

References pcl::computeMedian().

◆ computeMedianAbsoluteDeviation()

template<typename PointT >

double pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedianAbsoluteDeviation	(	const PointCloudConstPtr &	cloud,
		const IndicesPtr &	indices,
		double	sigma
	)		const

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 212 of file mlesac.hpp.

References pcl::computeMedian().

◆ computeModel()

template<typename PointT >

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

overridevirtual

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 50 of file mlesac.hpp.

References pcl::geometry::distance(), pcl::getMinMax(), and M_PI.

◆ getEMIterations()

template<typename PointT >

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

inline

Get the number of EM iterations.

Definition at line 112 of file mlesac.h.

◆ getMinMax()

template<typename PointT >

void pcl::MaximumLikelihoodSampleConsensus< PointT >::getMinMax	(	const PointCloudConstPtr &	cloud,
		const IndicesPtr &	indices,
		Eigen::Vector4f &	min_p,
		Eigen::Vector4f &	max_p
	)		const

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 237 of file mlesac.hpp.

◆ setEMIterations()

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 108 of file mlesac.h.

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

pcl/sample_consensus/mlesac.h
pcl/sample_consensus/impl/mlesac.hpp

Public Types

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

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

Member Typedef Documentation

◆ ConstPtr

◆ Ptr

Constructor & Destructor Documentation

◆ MaximumLikelihoodSampleConsensus() [1/2]

◆ MaximumLikelihoodSampleConsensus() [2/2]

Member Function Documentation

◆ computeMedian()

◆ computeMedianAbsoluteDeviation()

◆ computeModel()

◆ getEMIterations()

◆ getMinMax()

◆ setEMIterations()

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