SampleConsensus represents the base class. More...

#include <pcl/sample_consensus/sac.h>

Public Types
using	Ptr = shared_ptr< SampleConsensus< T > >

using	ConstPtr = shared_ptr< const SampleConsensus< T > >

Public Member Functions
	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...

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	computeModel (int debug_verbosity_level=0)=0
	Compute the actual model. 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	rnd ()
	Boost-based random number generator. More...

Protected Attributes
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 T>
class pcl::SampleConsensus< T >

SampleConsensus represents the base class.

All sample consensus methods must inherit from this class.

Author: Radu Bogdan Rusu

Definition at line 58 of file sac.h.

Member Typedef Documentation

◆ ConstPtr

template<typename T>

using pcl::SampleConsensus< T >::ConstPtr = shared_ptr<const SampleConsensus<T> >

Definition at line 68 of file sac.h.

◆ Ptr

template<typename T>

using pcl::SampleConsensus< T >::Ptr = shared_ptr<SampleConsensus<T> >

Definition at line 67 of file sac.h.

Constructor & Destructor Documentation

◆ SampleConsensus() [1/2]

template<typename T>

pcl::SampleConsensus< T >::SampleConsensus	(	const SampleConsensusModelPtr &	model,
		bool	random = `false`
	)

inline

Constructor for base SAC.

Parameters

[in]	model	a Sample Consensus model
[in]	random	if true set the random seed to the current time, else set to 12345 (default: false)

Definition at line 75 of file sac.h.

◆ SampleConsensus() [2/2]

template<typename T>

pcl::SampleConsensus< T >::SampleConsensus	(	const SampleConsensusModelPtr &	model,
		double	threshold,
		bool	random = `false`
	)

inline

Constructor for base SAC.

Parameters

[in]	model	a Sample Consensus model
[in]	threshold	distance to model threshold
[in]	random	if true set the random seed to the current time, else set to 12345 (default: false)

Definition at line 96 of file sac.h.

◆ ~SampleConsensus()

template<typename T>

virtual pcl::SampleConsensus< T >::~SampleConsensus ( )

inlinevirtual

Destructor for base SAC.

Definition at line 131 of file sac.h.

Member Function Documentation

◆ computeModel()

template<typename T>

virtual bool pcl::SampleConsensus< T >::computeModel ( int debug_verbosity_level = 0 )

pure virtual

Compute the actual model.

Pure virtual.

Implemented in pcl::MaximumLikelihoodSampleConsensus< PointT >, pcl::RandomizedMEstimatorSampleConsensus< PointT >, pcl::RandomizedRandomSampleConsensus< PointT >, pcl::RandomSampleConsensus< PointT >, pcl::MEstimatorSampleConsensus< PointT >, pcl::LeastMedianSquares< PointT >, and pcl::ProgressiveSampleConsensus< PointT >.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getNumberOfThreads().

◆ getDistanceThreshold()

template<typename T>

double pcl::SampleConsensus< T >::getDistanceThreshold ( ) const

inline

Get the distance to model threshold, as set by the user.

Definition at line 141 of file sac.h.

◆ getInliers()

template<typename T>

void pcl::SampleConsensus< T >::getInliers ( Indices & inliers ) const

inline

Return the best set of inliers found so far for this model.

Parameters

[out] inliers the resultant set of inliers

Definition at line 308 of file sac.h.

◆ getMaxIterations()

template<typename T>

int pcl::SampleConsensus< T >::getMaxIterations ( ) const

inline

Get the maximum number of iterations, as set by the user.

Definition at line 151 of file sac.h.

◆ getModel()

template<typename T>

void pcl::SampleConsensus< T >::getModel ( Indices & model ) const

inline

Return the best model found so far.

Parameters

[out] model the resultant model

Definition at line 302 of file sac.h.

◆ getModelCoefficients()

template<typename T>

void pcl::SampleConsensus< T >::getModelCoefficients ( Eigen::VectorXf & model_coefficients ) const

inline

Return the model coefficients of the best model found so far.

Parameters

[out] model_coefficients the resultant model coefficients, as documented in Module sample_consensus

Definition at line 314 of file sac.h.

◆ getNumberOfThreads()

template<typename T>

int pcl::SampleConsensus< T >::getNumberOfThreads ( ) const

inline

Get the number of threads, as set by the user.

Definition at line 173 of file sac.h.

◆ getProbability()

template<typename T>

double pcl::SampleConsensus< T >::getProbability ( ) const

inline

Obtain the probability of choosing at least one sample free from outliers, as set by the user.

Definition at line 162 of file sac.h.

◆ getRandomSamples()

template<typename T>

void pcl::SampleConsensus< T >::getRandomSamples	(	const IndicesPtr &	indices,
		std::size_t	nr_samples,
		std::set< index_t > &	indices_subset
	)

inline

Get a set of randomly selected indices.

Parameters

[in]	indices	the input indices vector
[in]	nr_samples	the desired number of point indices to randomly select
[out]	indices_subset	the resultant output set of randomly selected indices

Definition at line 288 of file sac.h.

◆ getSampleConsensusModel()

template<typename T>

SampleConsensusModelPtr pcl::SampleConsensus< T >::getSampleConsensusModel ( ) const

inline

Get the Sample Consensus model used.

Definition at line 125 of file sac.h.

◆ refineModel()

template<typename T>

virtual bool pcl::SampleConsensus< T >::refineModel	(	const double	sigma = `3.0`,
		const unsigned int	max_iterations = `1000`
	)

inlinevirtual

Refine the model found.

This loops over the model coefficients and optimizes them together with the set of inliers, until the change in the set of inliers is minimal.

Parameters

[in]	sigma	standard deviation multiplier for considering a sample as inlier (Mahalanobis distance)
[in]	max_iterations	the maxim number of iterations to try to refine in case the inliers keep on changing

Definition at line 187 of file sac.h.

◆ rnd()

template<typename T>

double pcl::SampleConsensus< T >::rnd ( )

inlineprotected

Boost-based random number generator.

Definition at line 352 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getRandomSamples().

◆ setDistanceThreshold()

template<typename T>

void pcl::SampleConsensus< T >::setDistanceThreshold ( double threshold )

inline

Set the distance to model threshold.

Parameters

[in] threshold distance to model threshold

Definition at line 137 of file sac.h.

◆ setMaxIterations()

template<typename T>

void pcl::SampleConsensus< T >::setMaxIterations ( int max_iterations )

inline

Set the maximum number of iterations.

Parameters

[in] max_iterations maximum number of iterations

Definition at line 147 of file sac.h.

◆ setNumberOfThreads()

template<typename T>

void pcl::SampleConsensus< T >::setNumberOfThreads ( const int nr_threads = -1 )

inline

Set the number of threads to use or turn off parallelization.

Parameters

[in] nr_threads the number of hardware threads to use (0 sets the value automatically, a negative number turns parallelization off)

Note: Not all SAC methods have a parallel implementation. Some will ignore this setting.

Definition at line 169 of file sac.h.

◆ setProbability()

template<typename T>

void pcl::SampleConsensus< T >::setProbability ( double probability )

inline

Set the desired probability of choosing at least one sample free from outliers.

Parameters

[in] probability the desired probability of choosing at least one sample free from outliers

Note: internally, the probability is set to 99% (0.99) by default.

Definition at line 158 of file sac.h.

◆ setSampleConsensusModel()

template<typename T>

void pcl::SampleConsensus< T >::setSampleConsensusModel ( const SampleConsensusModelPtr & model )

inline

Set the Sample Consensus model to use.

Parameters

[in] model a Sample Consensus model

Definition at line 118 of file sac.h.

Member Data Documentation

◆ inliers_

template<typename T>

Indices pcl::SampleConsensus< T >::inliers_

protected

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

Definition at line 324 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getInliers(), and pcl::SampleConsensus< WeightSACPointType >::refineModel().

◆ iterations_

template<typename T>

int pcl::SampleConsensus< T >::iterations_

protected

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

Definition at line 333 of file sac.h.

◆ max_iterations_

template<typename T>

int pcl::SampleConsensus< T >::max_iterations_

protected

Maximum number of iterations before giving up.

Definition at line 339 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getMaxIterations(), and pcl::SampleConsensus< WeightSACPointType >::setMaxIterations().

◆ model_

template<typename T>

Indices pcl::SampleConsensus< T >::model_

protected

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

Definition at line 321 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getModel().

◆ model_coefficients_

template<typename T>

Eigen::VectorXf pcl::SampleConsensus< T >::model_coefficients_

protected

The coefficients of our model computed directly from the model found.

Definition at line 327 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getModelCoefficients(), and pcl::SampleConsensus< WeightSACPointType >::refineModel().

◆ probability_

template<typename T>

double pcl::SampleConsensus< T >::probability_

protected

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

Definition at line 330 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getProbability(), and pcl::SampleConsensus< WeightSACPointType >::setProbability().

◆ rng_

template<typename T>

std::shared_ptr<boost::uniform_01<boost::mt19937> > pcl::SampleConsensus< T >::rng_

protected

Boost-based random number generator distribution.

Definition at line 348 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::SampleConsensus().

◆ rng_alg_

template<typename T>

boost::mt19937 pcl::SampleConsensus< T >::rng_alg_

protected

Boost-based random number generator algorithm.

Definition at line 345 of file sac.h.

◆ sac_model_

template<typename T>

SampleConsensusModelPtr pcl::SampleConsensus< T >::sac_model_

protected

The underlying data model used (i.e.

what is it that we attempt to search for).

Definition at line 318 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getSampleConsensusModel(), pcl::SampleConsensus< WeightSACPointType >::refineModel(), and pcl::SampleConsensus< WeightSACPointType >::setSampleConsensusModel().

◆ threads_

template<typename T>

int pcl::SampleConsensus< T >::threads_

protected

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

Definition at line 342 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getNumberOfThreads(), and pcl::SampleConsensus< WeightSACPointType >::setNumberOfThreads().

◆ threshold_

template<typename T>

double pcl::SampleConsensus< T >::threshold_

protected

Distance to model threshold.

Definition at line 336 of file sac.h.

Referenced by pcl::SampleConsensus< WeightSACPointType >::getDistanceThreshold(), pcl::SampleConsensus< WeightSACPointType >::refineModel(), and pcl::SampleConsensus< WeightSACPointType >::setDistanceThreshold().

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

pcl/sample_consensus/sac.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<typename T> class pcl::SampleConsensus< T >

Member Typedef Documentation

◆ ConstPtr

◆ Ptr

Constructor & Destructor Documentation

◆ SampleConsensus() [1/2]

◆ SampleConsensus() [2/2]

◆ ~SampleConsensus()

Member Function Documentation

◆ computeModel()

◆ getDistanceThreshold()

◆ getInliers()

◆ getMaxIterations()

◆ getModel()

◆ getModelCoefficients()

◆ getNumberOfThreads()

◆ getProbability()

◆ getRandomSamples()

◆ getSampleConsensusModel()

◆ refineModel()

◆ rnd()

◆ setDistanceThreshold()

◆ setMaxIterations()

◆ setNumberOfThreads()

◆ setProbability()

◆ setSampleConsensusModel()

Member Data Documentation

◆ inliers_

◆ iterations_

◆ max_iterations_

◆ model_

◆ model_coefficients_

◆ probability_

◆ rng_

◆ rng_alg_

◆ sac_model_

◆ threads_

◆ threshold_

template<typename T>
class pcl::SampleConsensus< T >