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 >:

Public Types
typedef Registration < PointSource, PointTarget, Scalar >::PointCloudSource	PointCloudSource
typedef PointCloudSource::Ptr	PointCloudSourcePtr
typedef PointCloudSource::ConstPtr	PointCloudSourceConstPtr
typedef Registration < PointSource, PointTarget, Scalar >::PointCloudTarget	PointCloudTarget
typedef PointCloudTarget::Ptr	PointCloudTargetPtr
typedef PointCloudTarget::ConstPtr	PointCloudTargetConstPtr
typedef PointIndices::Ptr	PointIndicesPtr
typedef PointIndices::ConstPtr	PointIndicesConstPtr
typedef boost::shared_ptr < IterativeClosestPoint < PointSource, PointTarget, Scalar > >	Ptr
typedef boost::shared_ptr < const IterativeClosestPoint < PointSource, PointTarget, Scalar > >	ConstPtr
typedef Registration < PointSource, PointTarget, Scalar >::Matrix4	Matrix4
Public Types inherited from pcl::Registration< PointSource, PointTarget, Scalar >
typedef Eigen::Matrix< Scalar, 4, 4 >	Matrix4
typedef boost::shared_ptr < Registration< PointSource, PointTarget, Scalar > >	Ptr
typedef boost::shared_ptr < const Registration < PointSource, PointTarget, Scalar > >	ConstPtr
typedef pcl::registration::CorrespondenceRejector::Ptr	CorrespondenceRejectorPtr
typedef pcl::search::KdTree < PointTarget >	KdTree
typedef pcl::search::KdTree < PointTarget >::Ptr	KdTreePtr
typedef pcl::search::KdTree < PointSource >	KdTreeReciprocal
typedef KdTree::Ptr	KdTreeReciprocalPtr
typedef pcl::PointCloud < PointSource >	PointCloudSource
typedef PointCloudSource::Ptr	PointCloudSourcePtr
typedef PointCloudSource::ConstPtr	PointCloudSourceConstPtr
typedef pcl::PointCloud < PointTarget >	PointCloudTarget
typedef PointCloudTarget::Ptr	PointCloudTargetPtr
typedef PointCloudTarget::ConstPtr	PointCloudTargetConstPtr
typedef KdTree::PointRepresentationConstPtr	PointRepresentationConstPtr
typedef pcl::registration::TransformationEstimation < PointSource, PointTarget, Scalar >	TransformationEstimation
typedef TransformationEstimation::Ptr	TransformationEstimationPtr
typedef TransformationEstimation::ConstPtr	TransformationEstimationConstPtr
typedef pcl::registration::CorrespondenceEstimationBase < PointSource, PointTarget, Scalar >	CorrespondenceEstimation
typedef CorrespondenceEstimation::Ptr	CorrespondenceEstimationPtr
typedef CorrespondenceEstimation::ConstPtr	CorrespondenceEstimationConstPtr
Public Types inherited from pcl::PCLBase< PointSource >
typedef pcl::PointCloud < PointSource >	PointCloud
typedef PointCloud::Ptr	PointCloudPtr
typedef PointCloud::ConstPtr	PointCloudConstPtr
typedef boost::shared_ptr < PointIndices >	PointIndicesPtr
typedef boost::shared_ptr < PointIndices const >	PointIndicesConstPtr

Public Member Functions
	IterativeClosestPoint ()
	Empty constructor.
virtual	~IterativeClosestPoint ()
	Empty destructor.
pcl::registration::DefaultConvergenceCriteria < Scalar >::Ptr	getConvergeCriteria ()
	Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
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)
virtual void	setInputTarget (const PointCloudTargetConstPtr &cloud)
	Provide a pointer to the input target (e.g., the point cloud that we want to align to the target)
void	setUseReciprocalCorrespondences (bool use_reciprocal_correspondence)
	Set whether to use reciprocal correspondence or not.
bool	getUseReciprocalCorrespondences () const
	Obtain whether reciprocal correspondence are used or not.
Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
	Registration ()
	Empty constructor.
virtual	~Registration ()
	destructor.
void	setTransformationEstimation (const TransformationEstimationPtr &te)
	Provide a pointer to the transformation estimation object.
void	setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce)
	Provide a pointer to the correspondence estimation object.
	PCL_DEPRECATED (void setInputCloud(const PointCloudSourceConstPtr &cloud),"[pcl::registration::Registration::setInputCloud] setInputCloud is deprecated. Please use setInputSource instead.")
	Provide a pointer to the input source (e.g., the point cloud that we want to align to the target)
	PCL_DEPRECATED (PointCloudSourceConstPtr const getInputCloud(),"[pcl::registration::Registration::getInputCloud] getInputCloud is deprecated. Please use getInputSource instead.")
	Get a pointer to the input point cloud dataset target.
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)
PointCloudSourceConstPtr const	getInputSource ()
	Get a pointer to the input point cloud dataset target.
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)
PointCloudTargetConstPtr const	getInputTarget ()
	Get a pointer to the input point cloud dataset target.
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.
KdTreePtr	getSearchMethodTarget () const
	Get a pointer to the search method used to find correspondences in the target cloud.
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).
KdTreeReciprocalPtr	getSearchMethodSource () const
	Get a pointer to the search method used to find correspondences in the source cloud.
Matrix4	getFinalTransformation ()
	Get the final transformation matrix estimated by the registration method.
Matrix4	getLastIncrementalTransformation ()
	Get the last incremental transformation matrix estimated by the registration method.
void	setMaximumIterations (int nr_iterations)
	Set the maximum number of iterations the internal optimization should run for.
int	getMaximumIterations ()
	Get the maximum number of iterations the internal optimization should run for, as set by the user.
void	setRANSACIterations (int ransac_iterations)
	Set the number of iterations RANSAC should run for.
double	getRANSACIterations ()
	Get the number of iterations RANSAC should run for, as set by the user.
void	setRANSACOutlierRejectionThreshold (double inlier_threshold)
	Set the inlier distance threshold for the internal RANSAC outlier rejection loop.
double	getRANSACOutlierRejectionThreshold ()
	Get the inlier distance threshold for the internal outlier rejection loop as set by the user.
void	setMaxCorrespondenceDistance (double distance_threshold)
	Set the maximum distance threshold between two correspondent points in source <-> target.
double	getMaxCorrespondenceDistance ()
	Get the maximum distance threshold between two correspondent points in source <-> target.
void	setTransformationEpsilon (double epsilon)
	Set the transformation epsilon (maximum allowable difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution.
double	getTransformationEpsilon ()
	Get the transformation epsilon (maximum allowable difference between two consecutive transformations) as set by the user.
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.
double	getEuclideanFitnessEpsilon ()
	Get the maximum allowed distance error before the algorithm will be considered to have converged, as set by the user.
void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a boost shared pointer to the PointRepresentation to be used when comparing points.
template<typename FunctionSignature >
bool	registerVisualizationCallback (boost::function< FunctionSignature > &visualizerCallback)
	Register the user callback function which will be called from registration thread in order to update point cloud obtained after each iteration.
double	getFitnessScore (double max_range=std::numeric_limits< double >::max())
	Obtain the Euclidean fitness score (e.g., sum of squared distances from the source to the target)
double	getFitnessScore (const std::vector< float > &distances_a, const std::vector< float > &distances_b)
	Obtain the Euclidean fitness score (e.g., sum of squared distances from the source to the target) from two sets of correspondence distances (distances between source and target points)
bool	hasConverged ()
	Return the state of convergence after the last align run.
void	align (PointCloudSource &output)
	Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output.
void	align (PointCloudSource &output, const Matrix4 &guess)
	Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output.
const std::string &	getClassName () const
	Abstract class get name method.
bool	initCompute ()
	Internal computation initalization.
bool	initComputeReciprocal ()
	Internal computation when reciprocal lookup is needed.
void	addCorrespondenceRejector (const CorrespondenceRejectorPtr &rejector)
	Add a new correspondence rejector to the list.
std::vector < CorrespondenceRejectorPtr >	getCorrespondenceRejectors ()
	Get the list of correspondence rejectors.
bool	removeCorrespondenceRejector (unsigned int i)
	Remove the i-th correspondence rejector in the list.
void	clearCorrespondenceRejectors ()
	Clear the list of correspondence rejectors.
Public Member Functions inherited from pcl::PCLBase< PointSource >
	PCLBase ()
	Empty constructor.
	PCLBase (const PCLBase &base)
	Copy constructor.
virtual	~PCLBase ()
	Destructor.
virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset.
PointCloudConstPtr const	getInputCloud ()
	Get a pointer to the input point cloud dataset.
virtual void	setIndices (const IndicesPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
virtual void	setIndices (const IndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
virtual void	setIndices (const PointIndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
virtual void	setIndices (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)
	Set the indices for the points laying within an interest region of the point cloud.
IndicesPtr const	getIndices ()
	Get a pointer to the vector of indices used.
const PointSource &	operator[] (size_t pos)
	Override PointCloud operator[] to shorten code.

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.
virtual void	computeTransformation (PointCloudSource &output, const Matrix4 &guess)
	Rigid transformation computation method with initial guess.
Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
bool	searchForNeighbors (const PointCloudSource &cloud, int index, std::vector< int > &indices, std::vector< float > &distances)
	Search for the closest nearest neighbor of a given point.
Protected Member Functions inherited from pcl::PCLBase< PointSource >
bool	deinitCompute ()
	This method should get called after finishing the actual computation.

Protected Attributes
size_t	x_idx_offset_
	XYZ fields offset.
size_t	y_idx_offset_
size_t	z_idx_offset_
size_t	nx_idx_offset_
	Normal fields offset.
size_t	ny_idx_offset_
size_t	nz_idx_offset_
bool	use_reciprocal_correspondence_
	The correspondence type used for correspondence estimation.
bool	source_has_normals_
	Internal check whether source dataset has normals or not.
bool	target_has_normals_
	Internal check whether target dataset has normals or not.
Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, Scalar >
std::string	reg_name_
	The registration method name.
KdTreePtr	tree_
	A pointer to the spatial search object.
KdTreeReciprocalPtr	tree_reciprocal_
	A pointer to the spatial search object of the source.
int	nr_iterations_
	The number of iterations the internal optimization ran for (used internally).
int	max_iterations_
	The maximum number of iterations the internal optimization should run for.
int	ransac_iterations_
	The number of iterations RANSAC should run for.
PointCloudTargetConstPtr	target_
	The input point cloud dataset target.
Matrix4	final_transformation_
	The final transformation matrix estimated by the registration method after N iterations.
Matrix4	transformation_
	The transformation matrix estimated by the registration method.
Matrix4	previous_transformation_
	The previous transformation matrix estimated by the registration method (used internally).
double	transformation_epsilon_
	The maximum difference between two consecutive transformations in order to consider convergence (user defined).
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.
double	corr_dist_threshold_
	The maximum distance threshold between two correspondent points in source <-> target.
double	inlier_threshold_
	The inlier distance threshold for the internal RANSAC outlier rejection loop.
bool	converged_
	Holds internal convergence state, given user parameters.
int	min_number_correspondences_
	The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation.
CorrespondencesPtr	correspondences_
	The set of correspondences determined at this ICP step.
TransformationEstimationPtr	transformation_estimation_
	A TransformationEstimation object, used to calculate the 4x4 rigid transformation.
CorrespondenceEstimationPtr	correspondence_estimation_
	A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud.
std::vector < CorrespondenceRejectorPtr >	correspondence_rejectors_
	The list of correspondence rejectors to use.
bool	target_cloud_updated_
	Variable that stores whether we have a new target cloud, meaning we need to pre-process it again.
bool	source_cloud_updated_
	Variable that stores whether we have a new source cloud, meaning we need to pre-process it again.
bool	force_no_recompute_
	A flag which, if set, means the tree operating on the target cloud will never be recomputed.
bool	force_no_recompute_reciprocal_
	A flag which, if set, means the tree operating on the source cloud will never be recomputed.
boost::function< void(const pcl::PointCloud< PointSource > &cloud_src, const std::vector < int > &indices_src, const pcl::PointCloud< PointTarget > &cloud_tgt, const std::vector < int > &indices_tgt)>	update_visualizer_
	Callback function to update intermediate source point cloud position during it's registration to the target point cloud.
Protected Attributes inherited from pcl::PCLBase< PointSource >
PointCloudConstPtr	input_
	The input point cloud dataset.
IndicesPtr	indices_
	A pointer to the vector of point indices to use.
bool	use_indices_
	Set to true if point indices are used.
bool	fake_indices_
	If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud.

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.setInputCloud (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 94 of file icp.h.

Member Typedef Documentation

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

typedef boost::shared_ptr<const IterativeClosestPoint<PointSource, PointTarget, Scalar> > pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::ConstPtr

Definition at line 109 of file icp.h.

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

typedef Registration<PointSource, PointTarget, Scalar>::Matrix4 pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4

Definition at line 135 of file icp.h.

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

typedef Registration<PointSource, PointTarget, Scalar>::PointCloudSource pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource

Definition at line 97 of file icp.h.

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

typedef PointCloudSource::ConstPtr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSourceConstPtr

Definition at line 99 of file icp.h.

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

typedef PointCloudSource::Ptr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSourcePtr

Definition at line 98 of file icp.h.

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

typedef Registration<PointSource, PointTarget, Scalar>::PointCloudTarget pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget

Definition at line 101 of file icp.h.

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

typedef PointCloudTarget::ConstPtr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTargetConstPtr

Definition at line 103 of file icp.h.

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

typedef PointCloudTarget::Ptr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTargetPtr

Definition at line 102 of file icp.h.

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

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

Definition at line 106 of file icp.h.

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

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

Definition at line 105 of file icp.h.

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

typedef boost::shared_ptr<IterativeClosestPoint<PointSource, PointTarget, Scalar> > pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::Ptr

Definition at line 108 of file icp.h.

Constructor & Destructor Documentation

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

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

inline

Empty constructor.

Definition at line 138 of file icp.h.

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

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

inlinevirtual

Empty destructor.

Definition at line 156 of file icp.h.

Member Function Documentation

template<typename PointSource , typename PointTarget , typename Scalar >

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

protectedvirtual

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

Implements pcl::Registration< PointSource, PointTarget, Scalar >.

Definition at line 119 of file icp.hpp.

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.

Parameters

[out] Pointer to the IterativeClosestPoint's DefaultConvergenceCriteria.

Definition at line 167 of file icp.h.

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 241 of file icp.h.

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

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

inlinevirtual

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

Definition at line 178 of file icp.h.

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

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

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

inlinevirtual

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

Parameters

[in] cloud the input point cloud target

Definition at line 213 of file icp.h.

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

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 234 of file icp.h.

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 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 49 of file icp.hpp.

References pcl::PointCloud< PointT >::size().

Member Data Documentation

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

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

Definition at line 134 of file icp.h.

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

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

size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nx_idx_offset_

protected

Normal fields offset.

Definition at line 271 of file icp.h.

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

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

size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::ny_idx_offset_

protected

Definition at line 271 of file icp.h.

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

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

size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nz_idx_offset_

protected

Definition at line 271 of file icp.h.

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

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

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

protected

Internal check whether source dataset has normals or not.

Definition at line 277 of file icp.h.

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

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

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

protected

Internal check whether target dataset has normals or not.

Definition at line 279 of file icp.h.

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

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

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

protected

The correspondence type used for correspondence estimation.

Definition at line 274 of file icp.h.

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

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

size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::x_idx_offset_

protected

XYZ fields offset.

Definition at line 268 of file icp.h.

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

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

size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::y_idx_offset_

protected

Definition at line 268 of file icp.h.

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

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

size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::z_idx_offset_

protected

Definition at line 268 of file icp.h.

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

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

/tmp/buildd/pcl-1.7-1.7.0/registration/include/pcl/registration/icp.h
/tmp/buildd/pcl-1.7-1.7.0/registration/include/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

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

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