Point Cloud Library (PCL)  1.9.1-dev
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pcl::MEstimatorSampleConsensus< PointT > Class Template Reference

MEstimatorSampleConsensus represents an implementation of the MSAC (M-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/msac.h>

+ Inheritance diagram for pcl::MEstimatorSampleConsensus< PointT >:

Public Types

using Ptr = boost::shared_ptr< MEstimatorSampleConsensus< PointT > >
 
using ConstPtr = boost::shared_ptr< const MEstimatorSampleConsensus< PointT > >
 
- Public Types inherited from pcl::SampleConsensus< PointT >
using Ptr = boost::shared_ptr< SampleConsensus< PointT > >
 
using ConstPtr = boost::shared_ptr< const SampleConsensus< PointT > >
 

Public Member Functions

 MEstimatorSampleConsensus (const SampleConsensusModelPtr &model)
 MSAC (M-estimator SAmple Consensus) main constructor. More...
 
 MEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold)
 MSAC (M-estimator SAmple Consensus) main constructor. More...
 
bool computeModel (int debug_verbosity_level=0) override
 Compute the actual model and find the inliers. 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 ()
 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...
 
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< int > &indices_subset)
 Get a set of randomly selected indices. More...
 
void getModel (std::vector< int > &model) const
 Return the best model found so far. More...
 
void getInliers (std::vector< int > &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...
 

Additional Inherited Members

- Protected Member Functions inherited from pcl::SampleConsensus< PointT >
double rnd ()
 Boost-based random number generator. More...
 
- Protected Attributes inherited from pcl::SampleConsensus< PointT >
SampleConsensusModelPtr sac_model_
 The underlying data model used (i.e. More...
 
std::vector< int > model_
 The model found after the last computeModel () as point cloud indices. More...
 
std::vector< int > 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...
 
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::MEstimatorSampleConsensus< PointT >

MEstimatorSampleConsensus represents an implementation of the MSAC (M-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.

Author
Radu B. Rusu

Definition at line 56 of file msac.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointT >
using pcl::MEstimatorSampleConsensus< PointT >::ConstPtr = boost::shared_ptr<const MEstimatorSampleConsensus<PointT> >

Definition at line 62 of file msac.h.

◆ Ptr

template<typename PointT >
using pcl::MEstimatorSampleConsensus< PointT >::Ptr = boost::shared_ptr<MEstimatorSampleConsensus<PointT> >

Definition at line 61 of file msac.h.

Constructor & Destructor Documentation

◆ MEstimatorSampleConsensus() [1/2]

template<typename PointT >
pcl::MEstimatorSampleConsensus< PointT >::MEstimatorSampleConsensus ( const SampleConsensusModelPtr &  model)
inline

MSAC (M-estimator SAmple Consensus) main constructor.

Parameters
[in]modela Sample Consensus model

Definition at line 76 of file msac.h.

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

◆ MEstimatorSampleConsensus() [2/2]

template<typename PointT >
pcl::MEstimatorSampleConsensus< PointT >::MEstimatorSampleConsensus ( const SampleConsensusModelPtr &  model,
double  threshold 
)
inline

MSAC (M-estimator SAmple Consensus) main constructor.

Parameters
[in]modela Sample Consensus model
[in]thresholddistance to model threshold

Definition at line 87 of file msac.h.

References pcl::MEstimatorSampleConsensus< PointT >::computeModel(), and pcl::SampleConsensus< PointT >::max_iterations_.

Member Function Documentation

◆ computeModel()

template<typename PointT >
bool pcl::MEstimatorSampleConsensus< PointT >::computeModel ( int  debug_verbosity_level = 0)
overridevirtual

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 48 of file msac.hpp.

References pcl::geometry::distance().

Referenced by pcl::MEstimatorSampleConsensus< PointT >::MEstimatorSampleConsensus().


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