IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm. More...

#include <pcl/registration/icp.h>

Inheritance diagram for pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >:

Collaboration diagram for pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >:

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

using	PointCloudSourcePtr = typename PointCloudSource::Ptr

using	PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

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

using	PointCloudTargetPtr = typename PointCloudTarget::Ptr

using	PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr

using	PointIndicesPtr = PointIndices::Ptr

using	PointIndicesConstPtr = PointIndices::ConstPtr

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

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

using	Matrix4 = typename Registration< PointSource, PointTarget, Scalar >::Matrix4

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

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

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

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, float >

using	TransformationEstimationPtr = typename TransformationEstimation::Ptr

using	TransformationEstimationConstPtr = typename TransformationEstimation::ConstPtr

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

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
	IterativeClosestPoint ()
	Empty constructor. More...

	IterativeClosestPoint (const IterativeClosestPoint &)=delete
	Due to `convergence_criteria_` holding references to the class members, it is tricky to correctly implement its copy and move operations correctly. More...

	IterativeClosestPoint (IterativeClosestPoint &&)=delete

IterativeClosestPoint &	operator= (const IterativeClosestPoint &)=delete

IterativeClosestPoint &	operator= (IterativeClosestPoint &&)=delete

	~IterativeClosestPoint () override=default
	Empty destructor. More...

pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr	getConvergeCriteria ()
	Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class. More...

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

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

void	setUseReciprocalCorrespondences (bool use_reciprocal_correspondence)
	Set whether to use reciprocal correspondence or not. More...

bool	getUseReciprocalCorrespondences () const
	Obtain whether reciprocal correspondence are used or not. More...

void	setNumberOfThreads (unsigned int nr_threads)
	Set the number of threads to use. More...

Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
	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...

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

Public Attributes
pcl::registration::DefaultConvergenceCriteria< Scalar >::Ptr	convergence_criteria_

Protected Member Functions
virtual void	transformCloud (const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
	Apply a rigid transform to a given dataset. More...

void	computeTransformation (PointCloudSource &output, const Matrix4 &guess) override
	Rigid transformation computation method with initial guess. More...

virtual void	determineRequiredBlobData ()
	Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called. More...

Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
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
std::size_t	x_idx_offset_ {0}
	XYZ fields offset. More...

std::size_t	y_idx_offset_ {0}

std::size_t	z_idx_offset_ {0}

std::size_t	nx_idx_offset_ {0}
	Normal fields offset. More...

std::size_t	ny_idx_offset_ {0}

std::size_t	nz_idx_offset_ {0}

bool	use_reciprocal_correspondence_ {false}
	The correspondence type used for correspondence estimation. More...

bool	source_has_normals_ {false}
	Internal check whether source dataset has normals or not. More...

bool	target_has_normals_ {false}
	Internal check whether target dataset has normals or not. More...

bool	need_source_blob_
	Checks for whether estimators and rejectors need various data. More...

bool	need_target_blob_

Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, float >
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_
	The number of iterations the internal optimization ran for (used internally). More...

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

int	ransac_iterations_
	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_
	The maximum difference between two consecutive transformations in order to consider convergence (user defined). More...

double	transformation_rotation_epsilon_
	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_
	The inlier distance threshold for the internal RANSAC outlier rejection loop. More...

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

unsigned int	min_number_correspondences_
	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_
	Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More...

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

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

bool	force_no_recompute_reciprocal_
	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 Scalar = float>
class pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >

IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm.

The transformation is estimated based on Singular Value Decomposition (SVD).

The algorithm has several termination criteria:

Number of iterations has reached the maximum user imposed number of iterations (via setMaximumIterations)
The epsilon (difference) between the previous transformation and the current estimated transformation is smaller than an user imposed value (via setTransformationEpsilon)
The sum of Euclidean squared errors is smaller than a user defined threshold (via setEuclideanFitnessEpsilon)

Usage example:

IterativeClosestPoint<PointXYZ, PointXYZ> icp;
// Set the input source and target
icp.setInputSource (cloud_source);
icp.setInputTarget (cloud_target);
 
// Set the max correspondence distance to 5cm (e.g., correspondences with higher
// distances will be ignored)
icp.setMaxCorrespondenceDistance (0.05);
// Set the maximum number of iterations (criterion 1)
icp.setMaximumIterations (50);
// Set the transformation epsilon (criterion 2)
icp.setTransformationEpsilon (1e-8);
// Set the euclidean distance difference epsilon (criterion 3)
icp.setEuclideanFitnessEpsilon (1);
 
// Perform the alignment
icp.align (cloud_source_registered);
 
// Obtain the transformation that aligned cloud_source to cloud_source_registered
Eigen::Matrix4f transformation = icp.getFinalTransformation ();

Author: Radu B. Rusu, Michael Dixon

Definition at line 98 of file icp.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::ConstPtr = shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> >

Definition at line 114 of file icp.h.

◆ Matrix4

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 = typename Registration<PointSource, PointTarget, Scalar>::Matrix4

Definition at line 143 of file icp.h.

◆ PointCloudSource

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource = typename Registration<PointSource, PointTarget, Scalar>::PointCloudSource

Definition at line 100 of file icp.h.

◆ PointCloudSourceConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

Definition at line 103 of file icp.h.

◆ PointCloudSourcePtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSourcePtr = typename PointCloudSource::Ptr

Definition at line 102 of file icp.h.

◆ PointCloudTarget

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget = typename Registration<PointSource, PointTarget, Scalar>::PointCloudTarget

Definition at line 105 of file icp.h.

◆ PointCloudTargetConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr

Definition at line 108 of file icp.h.

◆ PointCloudTargetPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTargetPtr = typename PointCloudTarget::Ptr

Definition at line 107 of file icp.h.

◆ PointIndicesConstPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointIndicesConstPtr = PointIndices::ConstPtr

Definition at line 111 of file icp.h.

◆ PointIndicesPtr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointIndicesPtr = PointIndices::Ptr

Definition at line 110 of file icp.h.

◆ Ptr

template<typename PointSource , typename PointTarget , typename Scalar = float>

using pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::Ptr = shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> >

Definition at line 113 of file icp.h.

Constructor & Destructor Documentation

◆ IterativeClosestPoint() [1/3]

template<typename PointSource , typename PointTarget , typename Scalar = float>

pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::IterativeClosestPoint ( )

inline

Empty constructor.

Definition at line 146 of file icp.h.

References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::convergence_criteria_, pcl::Registration< PointSource, PointTarget, float >::correspondence_estimation_, pcl::Registration< PointSource, PointTarget, float >::correspondences_, pcl::Registration< PointSource, PointTarget, float >::nr_iterations_, pcl::Registration< PointSource, PointTarget, float >::reg_name_, pcl::Registration< PointSource, PointTarget, float >::transformation_, and pcl::Registration< PointSource, PointTarget, float >::transformation_estimation_.

◆ IterativeClosestPoint() [2/3]

template<typename PointSource , typename PointTarget , typename Scalar = float>

pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::IterativeClosestPoint ( const IterativeClosestPoint< PointSource, PointTarget, Scalar > & )

delete

Due to convergence_criteria_ holding references to the class members, it is tricky to correctly implement its copy and move operations correctly.

This can result in subtle bugs and to prevent them, these operations for ICP have been disabled.

Todo:: : remove deleted ctors and assignments operations after resolving the issue

◆ IterativeClosestPoint() [3/3]

template<typename PointSource , typename PointTarget , typename Scalar = float>

pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::IterativeClosestPoint ( IterativeClosestPoint< PointSource, PointTarget, Scalar > && )

delete

◆ ~IterativeClosestPoint()

template<typename PointSource , typename PointTarget , typename Scalar = float>

pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::~IterativeClosestPoint ( )

overridedefault

Empty destructor.

Member Function Documentation

◆ computeTransformation()

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::computeTransformation	(	PointCloudSource &	output,
		const Matrix4 &	guess
	)

overrideprotected

Rigid transformation computation method with initial guess.

Parameters

output	the transformed input point cloud dataset using the rigid transformation found
guess	the initial guess of the transformation to compute

Definition at line 115 of file icp.hpp.

References pcl::toPCLPointCloud2().

◆ determineRequiredBlobData()

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::determineRequiredBlobData

protectedvirtual

Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called.

Reimplemented in pcl::JointIterativeClosestPoint< PointSource, PointTarget, Scalar >.

Definition at line 272 of file icp.hpp.

◆ getConvergeCriteria()

template<typename PointSource , typename PointTarget , typename Scalar = float>

pcl::registration::DefaultConvergenceCriteria<Scalar>::Ptr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::getConvergeCriteria ( )

inline

Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.

This allows to check the convergence state after the align() method as well as to configure DefaultConvergenceCriteria's parameters not available through the ICP API before the align() method is called. Please note that the align method sets max_iterations_, euclidean_fitness_epsilon_ and transformation_epsilon_ and therefore overrides the default / set values of the DefaultConvergenceCriteria instance.

Returns: Pointer to the IterativeClosestPoint's DefaultConvergenceCriteria.

Definition at line 188 of file icp.h.

References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::convergence_criteria_.

◆ getUseReciprocalCorrespondences()

template<typename PointSource , typename PointTarget , typename Scalar = float>

bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::getUseReciprocalCorrespondences ( ) const

inline

Obtain whether reciprocal correspondence are used or not.

Definition at line 259 of file icp.h.

References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::use_reciprocal_correspondence_.

◆ operator=() [1/2]

template<typename PointSource , typename PointTarget , typename Scalar = float>

IterativeClosestPoint& pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::operator= ( const IterativeClosestPoint< PointSource, PointTarget, Scalar > & )

delete

◆ operator=() [2/2]

template<typename PointSource , typename PointTarget , typename Scalar = float>

IterativeClosestPoint& pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::operator= ( IterativeClosestPoint< PointSource, PointTarget, Scalar > && )

delete

◆ setInputSource()

template<typename PointSource , typename PointTarget , typename Scalar = float>

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource ( const PointCloudSourceConstPtr & cloud )

inlineoverridevirtual

Provide a pointer to the input source (e.g., the point cloud that we want to align to the target)

Parameters

[in] cloud the input point cloud source

Reimplemented from pcl::Registration< PointSource, PointTarget, float >.

Definition at line 199 of file icp.h.

References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nx_idx_offset_, pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::ny_idx_offset_, pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nz_idx_offset_, pcl::Registration< PointSource, PointTarget, Scalar >::setInputSource(), pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::source_has_normals_, pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::x_idx_offset_, pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::y_idx_offset_, and pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::z_idx_offset_.

Referenced by pcl::JointIterativeClosestPoint< PointSource, PointTarget, Scalar >::addInputSource(), pcl::JointIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeTransformation(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ setInputTarget()

template<typename PointSource , typename PointTarget , typename Scalar = float>

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget ( const PointCloudTargetConstPtr & cloud )

inlineoverridevirtual

Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to)

Parameters

[in] cloud the input point cloud target

Reimplemented from pcl::Registration< PointSource, PointTarget, float >.

Definition at line 232 of file icp.h.

References pcl::Registration< PointSource, PointTarget, Scalar >::setInputTarget(), and pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::target_has_normals_.

Referenced by pcl::JointIterativeClosestPoint< PointSource, PointTarget, Scalar >::addInputTarget(), pcl::JointIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeTransformation(), and pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget().

◆ setNumberOfThreads()

template<typename PointSource , typename PointTarget , typename Scalar = float>

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setNumberOfThreads ( unsigned int nr_threads )

inline

Set the number of threads to use.

Parameters

nr_threads the number of hardware threads to use (0 sets the value back to automatic)

Definition at line 269 of file icp.h.

References pcl::Registration< PointSource, PointTarget, float >::correspondence_estimation_.

◆ setUseReciprocalCorrespondences()

template<typename PointSource , typename PointTarget , typename Scalar = float>

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setUseReciprocalCorrespondences ( bool use_reciprocal_correspondence )

inline

Set whether to use reciprocal correspondence or not.

Parameters

[in] use_reciprocal_correspondence whether to use reciprocal correspondence or not

Definition at line 252 of file icp.h.

References pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::use_reciprocal_correspondence_.

◆ transformCloud()

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::transformCloud	(	const PointCloudSource &	input,
		PointCloudSource &	output,
		const Matrix4 &	transform
	)

protectedvirtual

Apply a rigid transform to a given dataset.

Here we check whether the dataset has surface normals in addition to XYZ, and rotate normals as well.

Parameters

[in]	input	the input point cloud
[out]	output	the resultant output point cloud
[in]	transform	a 4x4 rigid transformation

Note: Can be used with cloud_in equal to cloud_out

Reimplemented in pcl::IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar >.

Definition at line 51 of file icp.hpp.

Member Data Documentation

◆ convergence_criteria_

template<typename PointSource , typename PointTarget , typename Scalar = float>

pcl::registration::DefaultConvergenceCriteria<Scalar>::Ptr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::convergence_criteria_

Definition at line 142 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::getConvergeCriteria(), and pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::IterativeClosestPoint().

◆ need_source_blob_

template<typename PointSource , typename PointTarget , typename Scalar = float>

bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::need_source_blob_

protected

Checks for whether estimators and rejectors need various data.

Definition at line 315 of file icp.h.

◆ need_target_blob_

template<typename PointSource , typename PointTarget , typename Scalar = float>

bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::need_target_blob_

protected

Definition at line 315 of file icp.h.

◆ nx_idx_offset_

template<typename PointSource , typename PointTarget , typename Scalar = float>

std::size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nx_idx_offset_ {0}

protected

Normal fields offset.

Definition at line 304 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ ny_idx_offset_

template<typename PointSource , typename PointTarget , typename Scalar = float>

std::size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::ny_idx_offset_ {0}

protected

Definition at line 304 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ nz_idx_offset_

template<typename PointSource , typename PointTarget , typename Scalar = float>

std::size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nz_idx_offset_ {0}

protected

Definition at line 304 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ source_has_normals_

template<typename PointSource , typename PointTarget , typename Scalar = float>

bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::source_has_normals_ {false}

protected

Internal check whether source dataset has normals or not.

Definition at line 310 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ target_has_normals_

template<typename PointSource , typename PointTarget , typename Scalar = float>

bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::target_has_normals_ {false}

protected

Internal check whether target dataset has normals or not.

Definition at line 312 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget().

◆ use_reciprocal_correspondence_

template<typename PointSource , typename PointTarget , typename Scalar = float>

bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::use_reciprocal_correspondence_ {false}

protected

The correspondence type used for correspondence estimation.

Definition at line 307 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::getUseReciprocalCorrespondences(), and pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setUseReciprocalCorrespondences().

◆ x_idx_offset_

template<typename PointSource , typename PointTarget , typename Scalar = float>

std::size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::x_idx_offset_ {0}

protected

XYZ fields offset.

Definition at line 301 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ y_idx_offset_

template<typename PointSource , typename PointTarget , typename Scalar = float>

std::size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::y_idx_offset_ {0}

protected

Definition at line 301 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

◆ z_idx_offset_

template<typename PointSource , typename PointTarget , typename Scalar = float>

std::size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::z_idx_offset_ {0}

protected

Definition at line 301 of file icp.h.

Referenced by pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource().

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

pcl/registration/icp.h
pcl/registration/impl/icp.hpp

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Detailed Description

template<typename PointSource, typename PointTarget, typename Scalar = float> class pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >

Member Typedef Documentation

◆ ConstPtr

◆ Matrix4

◆ PointCloudSource

◆ PointCloudSourceConstPtr

◆ PointCloudSourcePtr

◆ PointCloudTarget

◆ PointCloudTargetConstPtr

◆ PointCloudTargetPtr

◆ PointIndicesConstPtr

◆ PointIndicesPtr

◆ Ptr

Constructor & Destructor Documentation

◆ IterativeClosestPoint() [1/3]

◆ IterativeClosestPoint() [2/3]

◆ IterativeClosestPoint() [3/3]

◆ ~IterativeClosestPoint()

Member Function Documentation

◆ computeTransformation()

◆ determineRequiredBlobData()

◆ getConvergeCriteria()

◆ getUseReciprocalCorrespondences()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ setInputSource()

◆ setInputTarget()

◆ setNumberOfThreads()

◆ setUseReciprocalCorrespondences()

◆ transformCloud()

Member Data Documentation

◆ convergence_criteria_

◆ need_source_blob_

◆ need_target_blob_

◆ nx_idx_offset_

◆ ny_idx_offset_

◆ nz_idx_offset_

◆ source_has_normals_

◆ target_has_normals_

◆ use_reciprocal_correspondence_

◆ x_idx_offset_

◆ y_idx_offset_

◆ z_idx_offset_

template<typename PointSource, typename PointTarget, typename Scalar = float>
class pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >