Public Member Functions | Protected Member Functions

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>

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List of all members.

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 ()
 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 Bogdan Rusu

Definition at line 55 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:
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:
model a Sample Consensus model
threshold distance to model threshold

Definition at line 84 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:
cloud the point cloud data message
indices the point indices
median the resultant median value

Definition at line 248 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:
cloud the point cloud data message
indices the set of point indices to use
sigma the sigma value

Definition at line 191 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:
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 (  )  [inline]

Get the number of EM iterations.

Definition at line 102 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:
cloud the point cloud message
indices the set of point indices to use
min_p the resultant minimum bounding box coordinates
max_p the resultant maximum bounding box coordinates

Definition at line 224 of file mlesac.hpp.

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

Set the number of EM iterations.

Parameters:
iterations the number of EM iterations

Definition at line 99 of file mlesac.h.


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