Point Cloud Library (PCL)  1.7.0
Public Types | Public Member Functions | Protected Member Functions
pcl::MaximumLikelihoodSampleConsensus< PointT > Class Template Reference

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 >:

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]modela 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]modela Sample Consensus model
[in]thresholddistance 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 
) [protected]

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

Parameters:
[in]cloudthe point cloud data message
[in]indicesthe point indices
[out]medianthe resultant median value

Definition at line 264 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]cloudthe point cloud data message
[in]indicesthe set of point indices to use
[in]sigmathe sigma value

Definition at line 207 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_levelenable/disable on-screen debug information and set the verbosity level

Implements pcl::SampleConsensus< PointT >.

Definition at line 49 of file mlesac.hpp.

References pcl::getMinMax().

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 
) [protected]

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

Parameters:
[in]cloudthe point cloud message
[in]indicesthe set of point indices to use
[out]min_pthe resultant minimum bounding box coordinates
[out]max_pthe resultant maximum bounding box coordinates

Definition at line 240 of file mlesac.hpp.

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

Set the number of EM iterations.

Parameters:
[in]iterationsthe number of EM iterations

Definition at line 108 of file mlesac.h.


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