pcl::MaximumLikelihoodSampleConsensus< PointT > Class Template Reference
[Module sample_consensus]

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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List of all members.

Public Types
typedef boost::shared_ptr < MaximumLikelihoodSampleConsensus >	Ptr
typedef boost::shared_ptr < const MaximumLikelihoodSampleConsensus >	ConstPtr
Public Member Functions
	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.
bool	computeModel (int debug_verbosity_level=0)
	Compute the actual model and find the inliers.
void	setEMIterations (int iterations)
	Set the number of EM iterations.
int	getEMIterations () const
	Get the number of EM iterations.
Protected Member Functions
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.
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.

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

template<typename PointT >

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

Reimplemented from pcl::SampleConsensus< PointT >.

Definition at line 64 of file mlesac.h.

template<typename PointT >

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

Reimplemented from pcl::SampleConsensus< PointT >.

Definition at line 63 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 78 of file mlesac.h.

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

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

template<typename PointT >

void pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedian	(	const PointCloudConstPtr &	cloud,
		const boost::shared_ptr< std::vector< int > > &	indices,
		Eigen::Vector4f &	median
	)			`[inline, 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 264 of file mlesac.hpp.

Referenced by pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedianAbsoluteDeviation().

template<typename PointT >

double pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedianAbsoluteDeviation	(	const PointCloudConstPtr &	cloud,
		const boost::shared_ptr< std::vector< int > > &	indices,
		double	sigma
	)			`[inline, 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 207 of file mlesac.hpp.

References pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedian().

Referenced by pcl::MaximumLikelihoodSampleConsensus< PointT >::computeModel().

template<typename PointT >

bool pcl::MaximumLikelihoodSampleConsensus< PointT >::computeModel ( int debug_verbosity_level = 0 ) [inline, 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 49 of file mlesac.hpp.

template<typename PointT >

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

Get the number of EM iterations.

Definition at line 112 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
	)			`[inline, 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 240 of file mlesac.hpp.

Referenced by pcl::MaximumLikelihoodSampleConsensus< PointT >::computeModel().

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:

/builddir/build/BUILD/pcl-pcl-1.7.1/sample_consensus/include/pcl/sample_consensus/mlesac.h
/builddir/build/BUILD/pcl-pcl-1.7.1/sample_consensus/include/pcl/sample_consensus/impl/mlesac.hpp

pcl::MaximumLikelihoodSampleConsensus< PointT > Class Template Reference [Module sample_consensus]

Public Types

Public Member Functions

Protected Member Functions

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

pcl::MaximumLikelihoodSampleConsensus< PointT > Class Template Reference
[Module sample_consensus]

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