Point Cloud Library (PCL)  1.8.1-dev
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pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar > Class Template Reference

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 KdTreeReciprocal::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. More...
 
virtual ~IterativeClosestPoint ()
 Empty destructor. More...
 
pcl::registration::DefaultConvergenceCriteria
< Scalar >::Ptr 
getConvergeCriteria ()
 Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class. 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...
 
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) 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...
 
- Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
 Registration ()
 Empty constructor. More...
 
virtual ~Registration ()
 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...
 
void setInputCloud (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 getInputCloud ()
 Get a pointer to the input point cloud dataset target. 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...
 
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. More...
 
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) More...
 
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) More...
 
bool hasConverged ()
 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 initalization. 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 ()
 Destructor. 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 (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. More...
 
IndicesPtr const 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[] (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...
 
virtual void computeTransformation (PointCloudSource &output, const Matrix4 &guess)
 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, 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. 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

size_t x_idx_offset_
 XYZ fields offset. More...
 
size_t y_idx_offset_
 
size_t z_idx_offset_
 
size_t nx_idx_offset_
 Normal fields offset. More...
 
size_t ny_idx_offset_
 
size_t nz_idx_offset_
 
bool use_reciprocal_correspondence_
 The correspondence type used for correspondence estimation. More...
 
bool source_has_normals_
 Internal check whether source dataset has normals or not. More...
 
bool target_has_normals_
 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, 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_
 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...
 
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...
 
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. 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:

  1. Number of iterations has reached the maximum user imposed number of iterations (via setMaximumIterations)
  2. The epsilon (difference) between the previous transformation and the current estimated transformation is smaller than an user imposed value (via setTransformationEpsilon)
  3. 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
outputthe transformed input point cloud dataset using the rigid transformation found
guessthe initial guess of the transformation to compute

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

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

Definition at line 119 of file icp.hpp.

References pcl::toPCLPointCloud2().

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 254 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.

Returns
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]cloudthe input point cloud source

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

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

Definition at line 178 of file icp.h.

Referenced by pcl::GeneralizedIterativeClosestPoint< 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]cloudthe input point cloud target

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

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

Definition at line 213 of file icp.h.

Referenced by pcl::GeneralizedIterativeClosestPoint< 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_correspondencewhether 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]inputthe input point cloud
[out]outputthe resultant output point cloud
[in]transforma 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< T >::size().

Member Data Documentation

template<typename PointSource, typename PointTarget, typename Scalar = float>
pcl::registration::DefaultConvergenceCriteria<Scalar>::Ptr pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::convergence_criteria_
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 286 of file icp.h.

template<typename PointSource, typename PointTarget, typename Scalar = float>
bool pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::need_target_blob_
protected

Definition at line 286 of file icp.h.

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 275 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
template<typename PointSource, typename PointTarget, typename Scalar = float>
size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::nz_idx_offset_
protected
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 281 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 283 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
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 272 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
template<typename PointSource, typename PointTarget, typename Scalar = float>
size_t pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::z_idx_offset_
protected

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