SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al. More...

#include <pcl/registration/ia_ransac.h>

Inheritance diagram for pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >:

Collaboration diagram for pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >:

Classes
class	ErrorFunctor

class	HuberPenalty

class	TruncatedError

Public Types
using	PointCloudSource = typename Registration< PointSource, PointTarget >::PointCloudSource

using	PointCloudSourcePtr = typename PointCloudSource::Ptr

using	PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

using	PointCloudTarget = typename Registration< PointSource, PointTarget >::PointCloudTarget

using	PointIndicesPtr = PointIndices::Ptr

using	PointIndicesConstPtr = PointIndices::ConstPtr

using	FeatureCloud = pcl::PointCloud< FeatureT >

using	FeatureCloudPtr = typename FeatureCloud::Ptr

using	FeatureCloudConstPtr = typename FeatureCloud::ConstPtr

using	Ptr = shared_ptr< SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > >

using	ConstPtr = shared_ptr< const SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > >

using	ErrorFunctorPtr = typename ErrorFunctor::Ptr

using	FeatureKdTreePtr = typename KdTreeFLANN< FeatureT >::Ptr

Public Types inherited from pcl::Registration< PointSource, PointTarget, Scalar >
using	Matrix4 = Eigen::Matrix< Scalar, 4, 4 >

using	Ptr = shared_ptr< Registration< PointSource, PointTarget, Scalar > >

using	ConstPtr = shared_ptr< const Registration< PointSource, PointTarget, Scalar > >

using	CorrespondenceRejectorPtr = pcl::registration::CorrespondenceRejector::Ptr

using	KdTree = pcl::search::KdTree< PointTarget >

using	KdTreePtr = typename KdTree::Ptr

using	KdTreeReciprocal = pcl::search::KdTree< PointSource >

using	KdTreeReciprocalPtr = typename KdTreeReciprocal::Ptr

using	PointCloudSource = pcl::PointCloud< PointSource >

using	PointCloudSourcePtr = typename PointCloudSource::Ptr

using	PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

using	PointCloudTarget = pcl::PointCloud< PointTarget >

using	PointCloudTargetPtr = typename PointCloudTarget::Ptr

using	PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr

using	PointRepresentationConstPtr = typename KdTree::PointRepresentationConstPtr

using	TransformationEstimation = typename pcl::registration::TransformationEstimation< PointSource, PointTarget, Scalar >

using	TransformationEstimationPtr = typename TransformationEstimation::Ptr

using	TransformationEstimationConstPtr = typename TransformationEstimation::ConstPtr

using	CorrespondenceEstimation = pcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, Scalar >

using	CorrespondenceEstimationPtr = typename CorrespondenceEstimation::Ptr

using	CorrespondenceEstimationConstPtr = typename CorrespondenceEstimation::ConstPtr

using	UpdateVisualizerCallbackSignature = void(const pcl::PointCloud< PointSource > &, const pcl::Indices &, const pcl::PointCloud< PointTarget > &, const pcl::Indices &)
	The callback signature to the function updating intermediate source point cloud position during it's registration to the target point cloud. More...

Public Types inherited from pcl::PCLBase< PointSource >
using	PointCloud = pcl::PointCloud< PointSource >

using	PointCloudPtr = typename PointCloud::Ptr

using	PointCloudConstPtr = typename PointCloud::ConstPtr

using	PointIndicesPtr = PointIndices::Ptr

using	PointIndicesConstPtr = PointIndices::ConstPtr

Public Member Functions
	SampleConsensusInitialAlignment ()
	Constructor. More...

void	setSourceFeatures (const FeatureCloudConstPtr &features)
	Provide a shared pointer to the source point cloud's feature descriptors. More...

FeatureCloudConstPtr const	getSourceFeatures ()
	Get a pointer to the source point cloud's features. More...

void	setTargetFeatures (const FeatureCloudConstPtr &features)
	Provide a shared pointer to the target point cloud's feature descriptors. More...

FeatureCloudConstPtr const	getTargetFeatures ()
	Get a pointer to the target point cloud's features. More...

void	setMinSampleDistance (float min_sample_distance)
	Set the minimum distances between samples. More...

float	getMinSampleDistance ()
	Get the minimum distances between samples, as set by the user. More...

void	setNumberOfSamples (int nr_samples)
	Set the number of samples to use during each iteration. More...

int	getNumberOfSamples ()
	Get the number of samples to use during each iteration, as set by the user. More...

void	setCorrespondenceRandomness (int k)
	Set the number of neighbors to use when selecting a random feature correspondence. More...

int	getCorrespondenceRandomness ()
	Get the number of neighbors used when selecting a random feature correspondence, as set by the user. More...

void	setErrorFunction (const ErrorFunctorPtr &error_functor)
	Specify the error function to minimize. More...

ErrorFunctorPtr	getErrorFunction ()
	Get a shared pointer to the ErrorFunctor that is to be minimized. More...

Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
	Registration ()
	Empty constructor. More...

	~Registration () override=default
	destructor. More...

void	setTransformationEstimation (const TransformationEstimationPtr &te)
	Provide a pointer to the transformation estimation object. More...

void	setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce)
	Provide a pointer to the correspondence estimation object. More...

virtual void	setInputSource (const PointCloudSourceConstPtr &cloud)
	Provide a pointer to the input source (e.g., the point cloud that we want to align to the target) More...

PointCloudSourceConstPtr const	getInputSource ()
	Get a pointer to the input point cloud dataset target. More...

virtual void	setInputTarget (const PointCloudTargetConstPtr &cloud)
	Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) More...

PointCloudTargetConstPtr const	getInputTarget ()
	Get a pointer to the input point cloud dataset target. More...

void	setSearchMethodTarget (const KdTreePtr &tree, bool force_no_recompute=false)
	Provide a pointer to the search object used to find correspondences in the target cloud. More...

KdTreePtr	getSearchMethodTarget () const
	Get a pointer to the search method used to find correspondences in the target cloud. More...

void	setSearchMethodSource (const KdTreeReciprocalPtr &tree, bool force_no_recompute=false)
	Provide a pointer to the search object used to find correspondences in the source cloud (usually used by reciprocal correspondence finding). More...

KdTreeReciprocalPtr	getSearchMethodSource () const
	Get a pointer to the search method used to find correspondences in the source cloud. More...

Matrix4	getFinalTransformation ()
	Get the final transformation matrix estimated by the registration method. More...

Matrix4	getLastIncrementalTransformation ()
	Get the last incremental transformation matrix estimated by the registration method. More...

void	setMaximumIterations (int nr_iterations)
	Set the maximum number of iterations the internal optimization should run for. More...

int	getMaximumIterations ()
	Get the maximum number of iterations the internal optimization should run for, as set by the user. More...

void	setRANSACIterations (int ransac_iterations)
	Set the number of iterations RANSAC should run for. More...

double	getRANSACIterations ()
	Get the number of iterations RANSAC should run for, as set by the user. More...

void	setRANSACOutlierRejectionThreshold (double inlier_threshold)
	Set the inlier distance threshold for the internal RANSAC outlier rejection loop. More...

double	getRANSACOutlierRejectionThreshold ()
	Get the inlier distance threshold for the internal outlier rejection loop as set by the user. More...

void	setMaxCorrespondenceDistance (double distance_threshold)
	Set the maximum distance threshold between two correspondent points in source <-> target. More...

double	getMaxCorrespondenceDistance ()
	Get the maximum distance threshold between two correspondent points in source <-> target. More...

void	setTransformationEpsilon (double epsilon)
	Set the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More...

double	getTransformationEpsilon ()
	Get the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) as set by the user. More...

void	setTransformationRotationEpsilon (double epsilon)
	Set the transformation rotation epsilon (maximum allowable rotation difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More...

double	getTransformationRotationEpsilon ()
	Get the transformation rotation epsilon (maximum allowable difference between two consecutive transformations) as set by the user (epsilon is the cos(angle) in a axis-angle representation). More...

void	setEuclideanFitnessEpsilon (double epsilon)
	Set the maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More...

double	getEuclideanFitnessEpsilon ()
	Get the maximum allowed distance error before the algorithm will be considered to have converged, as set by the user. More...

void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a boost shared pointer to the PointRepresentation to be used when comparing points. More...

bool	registerVisualizationCallback (std::function< UpdateVisualizerCallbackSignature > &visualizerCallback)
	Register the user callback function which will be called from registration thread in order to update point cloud obtained after each iteration. More...

double	getFitnessScore (double max_range=std::numeric_limits< double >::max())
	Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) More...

double	getFitnessScore (const std::vector< float > &distances_a, const std::vector< float > &distances_b)
	Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) from two sets of correspondence distances (distances between source and target points) More...

bool	hasConverged () const
	Return the state of convergence after the last align run. More...

void	align (PointCloudSource &output)
	Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More...

void	align (PointCloudSource &output, const Matrix4 &guess)
	Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More...

const std::string &	getClassName () const
	Abstract class get name method. More...

bool	initCompute ()
	Internal computation initialization. More...

bool	initComputeReciprocal ()
	Internal computation when reciprocal lookup is needed. More...

void	addCorrespondenceRejector (const CorrespondenceRejectorPtr &rejector)
	Add a new correspondence rejector to the list. More...

std::vector< CorrespondenceRejectorPtr >	getCorrespondenceRejectors ()
	Get the list of correspondence rejectors. More...

bool	removeCorrespondenceRejector (unsigned int i)
	Remove the i-th correspondence rejector in the list. More...

void	clearCorrespondenceRejectors ()
	Clear the list of correspondence rejectors. More...

Public Member Functions inherited from pcl::PCLBase< PointSource >
	PCLBase ()
	Empty constructor. More...

	PCLBase (const PCLBase &base)
	Copy constructor. More...

virtual	~PCLBase ()=default
	Destructor. More...

virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset. More...

PointCloudConstPtr const	getInputCloud () const
	Get a pointer to the input point cloud dataset. More...

virtual void	setIndices (const IndicesPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (const IndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (const PointIndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols)
	Set the indices for the points laying within an interest region of the point cloud. More...

IndicesPtr	getIndices ()
	Get a pointer to the vector of indices used. More...

IndicesConstPtr const	getIndices () const
	Get a pointer to the vector of indices used. More...

const PointSource &	operator[] (std::size_t pos) const
	Override PointCloud operator[] to shorten code. More...

Protected Member Functions
pcl::index_t	getRandomIndex (int n)
	Choose a random index between 0 and n-1. More...

void	selectSamples (const PointCloudSource &cloud, unsigned int nr_samples, float min_sample_distance, pcl::Indices &sample_indices)
	Select nr_samples sample points from cloud while making sure that their pairwise distances are greater than a user-defined minimum distance, min_sample_distance. More...

void	findSimilarFeatures (const FeatureCloud &input_features, const pcl::Indices &sample_indices, pcl::Indices &corresponding_indices)
	For each of the sample points, find a list of points in the target cloud whose features are similar to the sample points' features. More...

float	computeErrorMetric (const PointCloudSource &cloud, float threshold)
	An error metric for that computes the quality of the alignment between the given cloud and the target. More...

void	computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess) override
	Rigid transformation computation method. More...

Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
bool	searchForNeighbors (const PointCloudSource &cloud, int index, pcl::Indices &indices, std::vector< float > &distances)
	Search for the closest nearest neighbor of a given point. More...

virtual void	computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0
	Abstract transformation computation method with initial guess. More...

Protected Member Functions inherited from pcl::PCLBase< PointSource >
bool	initCompute ()
	This method should get called before starting the actual computation. More...

bool	deinitCompute ()
	This method should get called after finishing the actual computation. More...

Protected Attributes
FeatureCloudConstPtr	input_features_
	The source point cloud's feature descriptors. More...

FeatureCloudConstPtr	target_features_
	The target point cloud's feature descriptors. More...

int	nr_samples_ {3}
	The number of samples to use during each iteration. More...

float	min_sample_distance_ {0.0f}
	The minimum distances between samples. More...

int	k_correspondences_ {10}
	The number of neighbors to use when selecting a random feature correspondence. More...

FeatureKdTreePtr	feature_tree_
	The KdTree used to compare feature descriptors. More...

ErrorFunctorPtr	error_functor_

Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, Scalar >
std::string	reg_name_
	The registration method name. More...

KdTreePtr	tree_
	A pointer to the spatial search object. More...

KdTreeReciprocalPtr	tree_reciprocal_
	A pointer to the spatial search object of the source. More...

int	nr_iterations_ {0}
	The number of iterations the internal optimization ran for (used internally). More...

int	max_iterations_ {10}
	The maximum number of iterations the internal optimization should run for. More...

int	ransac_iterations_ {0}
	The number of iterations RANSAC should run for. More...

PointCloudTargetConstPtr	target_
	The input point cloud dataset target. More...

Matrix4	final_transformation_
	The final transformation matrix estimated by the registration method after N iterations. More...

Matrix4	transformation_
	The transformation matrix estimated by the registration method. More...

Matrix4	previous_transformation_
	The previous transformation matrix estimated by the registration method (used internally). More...

double	transformation_epsilon_ {0.0}
	The maximum difference between two consecutive transformations in order to consider convergence (user defined). More...

double	transformation_rotation_epsilon_ {0.0}
	The maximum rotation difference between two consecutive transformations in order to consider convergence (user defined). More...

double	euclidean_fitness_epsilon_
	The maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More...

double	corr_dist_threshold_
	The maximum distance threshold between two correspondent points in source <-> target. More...

double	inlier_threshold_ {0.05}
	The inlier distance threshold for the internal RANSAC outlier rejection loop. More...

bool	converged_ {false}
	Holds internal convergence state, given user parameters. More...

unsigned int	min_number_correspondences_ {3}
	The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More...

CorrespondencesPtr	correspondences_
	The set of correspondences determined at this ICP step. More...

TransformationEstimationPtr	transformation_estimation_
	A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More...

CorrespondenceEstimationPtr	correspondence_estimation_
	A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More...

std::vector< CorrespondenceRejectorPtr >	correspondence_rejectors_
	The list of correspondence rejectors to use. More...

bool	target_cloud_updated_ {true}
	Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More...

bool	source_cloud_updated_ {true}
	Variable that stores whether we have a new source cloud, meaning we need to pre-process it again. More...

bool	force_no_recompute_ {false}
	A flag which, if set, means the tree operating on the target cloud will never be recomputed. More...

bool	force_no_recompute_reciprocal_ {false}
	A flag which, if set, means the tree operating on the source cloud will never be recomputed. More...

std::function< UpdateVisualizerCallbackSignature >	update_visualizer_
	Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More...

Protected Attributes inherited from pcl::PCLBase< PointSource >
PointCloudConstPtr	input_
	The input point cloud dataset. More...

IndicesPtr	indices_
	A pointer to the vector of point indices to use. More...

bool	use_indices_
	Set to true if point indices are used. More...

bool	fake_indices_
	If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More...

Detailed Description

template<typename PointSource, typename PointTarget, typename FeatureT>
class pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >

SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al.

Author: Michael Dixon, Radu B. Rusu

Definition at line 54 of file ia_ransac.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ConstPtr = shared_ptr< const SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT> >

Definition at line 87 of file ia_ransac.h.

◆ ErrorFunctorPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctorPtr = typename ErrorFunctor::Ptr

Definition at line 138 of file ia_ransac.h.

◆ FeatureCloud

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureCloud = pcl::PointCloud<FeatureT>

Definition at line 81 of file ia_ransac.h.

◆ FeatureCloudConstPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureCloudConstPtr = typename FeatureCloud::ConstPtr

Definition at line 83 of file ia_ransac.h.

◆ FeatureCloudPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureCloudPtr = typename FeatureCloud::Ptr

Definition at line 82 of file ia_ransac.h.

◆ FeatureKdTreePtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::FeatureKdTreePtr = typename KdTreeFLANN<FeatureT>::Ptr

Definition at line 140 of file ia_ransac.h.

◆ PointCloudSource

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudSource = typename Registration<PointSource, PointTarget>::PointCloudSource

Definition at line 70 of file ia_ransac.h.

◆ PointCloudSourceConstPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

Definition at line 73 of file ia_ransac.h.

◆ PointCloudSourcePtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudSourcePtr = typename PointCloudSource::Ptr

Definition at line 72 of file ia_ransac.h.

◆ PointCloudTarget

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointCloudTarget = typename Registration<PointSource, PointTarget>::PointCloudTarget

Definition at line 75 of file ia_ransac.h.

◆ PointIndicesConstPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointIndicesConstPtr = PointIndices::ConstPtr

Definition at line 79 of file ia_ransac.h.

◆ PointIndicesPtr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::PointIndicesPtr = PointIndices::Ptr

Definition at line 78 of file ia_ransac.h.

◆ Ptr

template<typename PointSource , typename PointTarget , typename FeatureT >

using pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::Ptr = shared_ptr<SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT> >

Definition at line 85 of file ia_ransac.h.

Constructor & Destructor Documentation

◆ SampleConsensusInitialAlignment()

template<typename PointSource , typename PointTarget , typename FeatureT >

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::SampleConsensusInitialAlignment ( )

inline

Constructor.

Definition at line 142 of file ia_ransac.h.

References pcl::Registration< PointSource, PointTarget, Scalar >::corr_dist_threshold_, pcl::Registration< PointSource, PointTarget, Scalar >::max_iterations_, pcl::Registration< PointSource, PointTarget, Scalar >::reg_name_, and pcl::Registration< PointSource, PointTarget, Scalar >::transformation_estimation_.

Member Function Documentation

◆ computeErrorMetric()

template<typename PointSource , typename PointTarget , typename FeatureT >

float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::computeErrorMetric	(	const PointCloudSource &	cloud,
		float	threshold
	)

protected

An error metric for that computes the quality of the alignment between the given cloud and the target.

Parameters

cloud	the input cloud
threshold	distances greater than this value are capped

Definition at line 166 of file ia_ransac.hpp.

◆ computeTransformation()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::computeTransformation	(	PointCloudSource &	output,
		const Eigen::Matrix4f &	guess
	)

overrideprotected

Rigid transformation computation method.

Parameters

output	the transformed input point cloud dataset using the rigid transformation found
guess	The computed transforamtion

Definition at line 188 of file ia_ransac.hpp.

References pcl::transformPointCloud().

◆ findSimilarFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::findSimilarFeatures	(	const FeatureCloud &	input_features,
		const pcl::Indices &	sample_indices,
		pcl::Indices &	corresponding_indices
	)

protected

For each of the sample points, find a list of points in the target cloud whose features are similar to the sample points' features.

From these, select one randomly which will be considered that sample point's correspondence.

Parameters

input_features	a cloud of feature descriptors
sample_indices	the indices of each sample point
corresponding_indices	the resulting indices of each sample's corresponding point in the target cloud

Definition at line 141 of file ia_ransac.hpp.

◆ getCorrespondenceRandomness()

template<typename PointSource , typename PointTarget , typename FeatureT >

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getCorrespondenceRandomness ( )

inline

Get the number of neighbors used when selecting a random feature correspondence, as set by the user.

Definition at line 231 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::k_correspondences_.

◆ getErrorFunction()

template<typename PointSource , typename PointTarget , typename FeatureT >

ErrorFunctorPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getErrorFunction ( )

inline

Get a shared pointer to the ErrorFunctor that is to be minimized.

Returns: A shared pointer to a subclass of SampleConsensusInitialAlignment::ErrorFunctor

Definition at line 252 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_.

◆ getMinSampleDistance()

template<typename PointSource , typename PointTarget , typename FeatureT >

float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getMinSampleDistance ( )

inline

Get the minimum distances between samples, as set by the user.

Definition at line 195 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_.

◆ getNumberOfSamples()

template<typename PointSource , typename PointTarget , typename FeatureT >

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getNumberOfSamples ( )

inline

Get the number of samples to use during each iteration, as set by the user.

Definition at line 212 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::nr_samples_.

◆ getRandomIndex()

template<typename PointSource , typename PointTarget , typename FeatureT >

pcl::index_t pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getRandomIndex ( int n )

inlineprotected

Choose a random index between 0 and n-1.

Parameters

n	the number of possible indices to choose from

Definition at line 262 of file ia_ransac.h.

◆ getSourceFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >

FeatureCloudConstPtr const pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getSourceFeatures ( )

inline

Get a pointer to the source point cloud's features.

Definition at line 166 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::input_features_.

◆ getTargetFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >

FeatureCloudConstPtr const pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getTargetFeatures ( )

inline

Get a pointer to the target point cloud's features.

Definition at line 179 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::target_features_.

◆ selectSamples()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::selectSamples	(	const PointCloudSource &	cloud,
		unsigned int	nr_samples,
		float	min_sample_distance,
		pcl::Indices &	sample_indices
	)

protected

Select nr_samples sample points from cloud while making sure that their pairwise distances are greater than a user-defined minimum distance, min_sample_distance.

Parameters

cloud	the input point cloud
nr_samples	the number of samples to select
min_sample_distance	the minimum distance between any two samples
sample_indices	the resulting sample indices

Definition at line 79 of file ia_ransac.hpp.

References pcl::euclideanDistance().

◆ setCorrespondenceRandomness()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setCorrespondenceRandomness ( int k )

inline

Set the number of neighbors to use when selecting a random feature correspondence.

A higher value will add more randomness to the feature matching.

Parameters

k	the number of neighbors to use when selecting a random feature correspondence.

Definition at line 223 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::k_correspondences_.

◆ setErrorFunction()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setErrorFunction ( const ErrorFunctorPtr & error_functor )

inline

Specify the error function to minimize.

Note: This call is optional. TruncatedError will be used by default

Parameters

[in] error_functor a shared pointer to a subclass of SampleConsensusInitialAlignment::ErrorFunctor

Definition at line 242 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_.

◆ setMinSampleDistance()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setMinSampleDistance ( float min_sample_distance )

inline

Set the minimum distances between samples.

Parameters

min_sample_distance the minimum distances between samples

Definition at line 188 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_.

◆ setNumberOfSamples()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setNumberOfSamples ( int nr_samples )

inline

Set the number of samples to use during each iteration.

Parameters

nr_samples the number of samples to use during each iteration

Definition at line 204 of file ia_ransac.h.

References pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::nr_samples_.

◆ setSourceFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setSourceFeatures ( const FeatureCloudConstPtr & features )

Provide a shared pointer to the source point cloud's feature descriptors.

Parameters

features the source point cloud's features

Definition at line 50 of file ia_ransac.hpp.

◆ setTargetFeatures()

template<typename PointSource , typename PointTarget , typename FeatureT >

void pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setTargetFeatures ( const FeatureCloudConstPtr & features )

Provide a shared pointer to the target point cloud's feature descriptors.

Parameters

features the target point cloud's features

Definition at line 64 of file ia_ransac.hpp.

Member Data Documentation

◆ error_functor_

template<typename PointSource , typename PointTarget , typename FeatureT >

ErrorFunctorPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::error_functor_

protected

Definition at line 325 of file ia_ransac.h.

Referenced by pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getErrorFunction(), and pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setErrorFunction().

◆ feature_tree_

template<typename PointSource , typename PointTarget , typename FeatureT >

FeatureKdTreePtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::feature_tree_

protected

The KdTree used to compare feature descriptors.

Definition at line 323 of file ia_ransac.h.

◆ input_features_

template<typename PointSource , typename PointTarget , typename FeatureT >

FeatureCloudConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::input_features_

protected

The source point cloud's feature descriptors.

Definition at line 307 of file ia_ransac.h.

Referenced by pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getSourceFeatures().

◆ k_correspondences_

template<typename PointSource , typename PointTarget , typename FeatureT >

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::k_correspondences_ {10}

protected

The number of neighbors to use when selecting a random feature correspondence.

Definition at line 320 of file ia_ransac.h.

Referenced by pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getCorrespondenceRandomness(), and pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setCorrespondenceRandomness().

◆ min_sample_distance_

template<typename PointSource , typename PointTarget , typename FeatureT >

float pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::min_sample_distance_ {0.0f}

protected

The minimum distances between samples.

Definition at line 316 of file ia_ransac.h.

Referenced by pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getMinSampleDistance(), and pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setMinSampleDistance().

◆ nr_samples_

template<typename PointSource , typename PointTarget , typename FeatureT >

int pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::nr_samples_ {3}

protected

The number of samples to use during each iteration.

Definition at line 313 of file ia_ransac.h.

Referenced by pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getNumberOfSamples(), and pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::setNumberOfSamples().

◆ target_features_

template<typename PointSource , typename PointTarget , typename FeatureT >

FeatureCloudConstPtr pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::target_features_

protected

The target point cloud's feature descriptors.

Definition at line 310 of file ia_ransac.h.

Referenced by pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::getTargetFeatures().

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

pcl/registration/ia_ransac.h
pcl/registration/impl/ia_ransac.hpp

Classes

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<typename PointSource, typename PointTarget, typename FeatureT> class pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >

Member Typedef Documentation

◆ ConstPtr

◆ ErrorFunctorPtr

◆ FeatureCloud

◆ FeatureCloudConstPtr

◆ FeatureCloudPtr

◆ FeatureKdTreePtr

◆ PointCloudSource

◆ PointCloudSourceConstPtr

◆ PointCloudSourcePtr

◆ PointCloudTarget

◆ PointIndicesConstPtr

◆ PointIndicesPtr

◆ Ptr

Constructor & Destructor Documentation

◆ SampleConsensusInitialAlignment()

Member Function Documentation

◆ computeErrorMetric()

◆ computeTransformation()

◆ findSimilarFeatures()

◆ getCorrespondenceRandomness()

◆ getErrorFunction()

◆ getMinSampleDistance()

◆ getNumberOfSamples()

◆ getRandomIndex()

◆ getSourceFeatures()

◆ getTargetFeatures()

◆ selectSamples()

◆ setCorrespondenceRandomness()

◆ setErrorFunction()

◆ setMinSampleDistance()

◆ setNumberOfSamples()

◆ setSourceFeatures()

◆ setTargetFeatures()

Member Data Documentation

◆ error_functor_

◆ feature_tree_

◆ input_features_

◆ k_correspondences_

◆ min_sample_distance_

◆ nr_samples_

◆ target_features_

template<typename PointSource, typename PointTarget, typename FeatureT>
class pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >