GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al. More...

#include <pcl/registration/gicp.h>

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

Classes
struct	OptimizationFunctorWithIndices
	optimization functor structure More...

Public Types
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	PointIndicesPtr = PointIndices::Ptr
using	PointIndicesConstPtr = PointIndices::ConstPtr
using	MatricesVector
using	MatricesVectorPtr = shared_ptr<MatricesVector>
using	MatricesVectorConstPtr = shared_ptr<const MatricesVector>
using	InputKdTree = typename Registration<PointSource, PointTarget, Scalar>::KdTree
using	InputKdTreePtr
using	Ptr
using	ConstPtr
using	Vector3 = typename Eigen::Matrix<Scalar, 3, 1>
using	Vector4 = typename Eigen::Matrix<Scalar, 4, 1>
using	Vector6d = Eigen::Matrix<double, 6, 1>
using	Matrix3 = typename Eigen::Matrix<Scalar, 3, 3>
using	Matrix4
using	Matrix6d = Eigen::Matrix<double, 6, 6>
using	AngleAxis = typename Eigen::AngleAxis<Scalar>
Public Types inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
using	PointCloudSource
using	PointCloudSourcePtr
using	PointCloudSourceConstPtr
using	PointCloudTarget
using	PointCloudTargetPtr
using	PointCloudTargetConstPtr
using	PointIndicesPtr
using	PointIndicesConstPtr
using	Ptr
using	ConstPtr
using	Matrix4
Public Types inherited from pcl::Registration< PointSource, PointTarget, float >
using	Matrix4
using	Ptr
using	ConstPtr
using	CorrespondenceRejectorPtr
using	KdTree
using	KdTreePtr
using	KdTreeReciprocal
using	KdTreeReciprocalPtr
using	PointCloudSource
using	PointCloudSourcePtr
using	PointCloudSourceConstPtr
using	PointCloudTarget
using	PointCloudTargetPtr
using	PointCloudTargetConstPtr
using	PointRepresentationConstPtr
using	TransformationEstimation
using	TransformationEstimationPtr
using	TransformationEstimationConstPtr
using	CorrespondenceEstimation
using	CorrespondenceEstimationPtr
using	CorrespondenceEstimationConstPtr
using	UpdateVisualizerCallbackSignature
	The callback signature to the function updating intermediate source point cloud position during it's registration to the target point cloud.
Public Types inherited from pcl::PCLBase< PointSource >
using	PointCloud
using	PointCloudPtr
using	PointCloudConstPtr
using	PointIndicesPtr
using	PointIndicesConstPtr

Public Member Functions
PCL_MAKE_ALIGNED_OPERATOR_NEW	GeneralizedIterativeClosestPoint ()
	Empty constructor.
void	setInputSource (const PointCloudSourceConstPtr &cloud) override
	Provide a pointer to the input dataset.
void	setSourceCovariances (const MatricesVectorPtr &covariances)
	Provide a pointer to the covariances of the input source (if computed externally!).
void	setInputTarget (const PointCloudTargetConstPtr &target) override
	Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to).
void	setTargetCovariances (const MatricesVectorPtr &covariances)
	Provide a pointer to the covariances of the input target (if computed externally!).
void	estimateRigidTransformationBFGS (const PointCloudSource &cloud_src, const pcl::Indices &indices_src, const PointCloudTarget &cloud_tgt, const pcl::Indices &indices_tgt, Matrix4 &transformation_matrix)
	Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear BFGS approach.
void	estimateRigidTransformationNewton (const PointCloudSource &cloud_src, const pcl::Indices &indices_src, const PointCloudTarget &cloud_tgt, const pcl::Indices &indices_tgt, Matrix4 &transformation_matrix)
	Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Newton approach.
const Eigen::Matrix3d &	mahalanobis (std::size_t index) const
void	computeRDerivative (const Vector6d &x, const Eigen::Matrix3d &dCost_dR_T, Vector6d &g) const
	Computes the derivative of the cost function w.r.t rotation angles.
void	setRotationEpsilon (double epsilon)
	Set the rotation epsilon (maximum allowable difference between two consecutive rotations) in order for an optimization to be considered as having converged to the final solution.
double	getRotationEpsilon () const
	Get the rotation epsilon (maximum allowable difference between two consecutive rotations) as set by the user.
void	setCorrespondenceRandomness (int k)
	Set the number of neighbors used when selecting a point neighbourhood to compute covariances.
int	getCorrespondenceRandomness () const
	Get the number of neighbors used when computing covariances as set by the user.
void	useBFGS ()
	Use BFGS optimizer instead of default Newton optimizer.
void	setMaximumOptimizerIterations (int max)
	Set maximum number of iterations at the optimization step.
int	getMaximumOptimizerIterations () const
	Return maximum number of iterations at the optimization step.
void	setTranslationGradientTolerance (double tolerance)
	Set the minimal translation gradient threshold for early optimization stop.
double	getTranslationGradientTolerance () const
	Return the minimal translation gradient threshold for early optimization stop.
void	setRotationGradientTolerance (double tolerance)
	Set the minimal rotation gradient threshold for early optimization stop.
double	getRotationGradientTolerance () const
	Return the minimal rotation gradient threshold for early optimization stop.
void	setNumberOfThreads (unsigned int nr_threads=0)
	Initialize the scheduler and set the number of threads to use.
Public Member Functions inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
	IterativeClosestPoint ()
	Empty constructor.
IterativeClosestPoint &	operator= (const IterativeClosestPoint &)=delete
	~IterativeClosestPoint () override=default
	Empty destructor.
pcl::registration::DefaultConvergenceCriteria< float >::Ptr	getConvergeCriteria ()
	Returns a pointer to the DefaultConvergenceCriteria used by the IterativeClosestPoint class.
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).
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).
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.
void	setNumberOfThreads (unsigned int nr_threads)
	Set the number of threads to use.
Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
	Registration ()
	Empty constructor.
	~Registration () override=default
	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.
PointCloudSourceConstPtr const	getInputSource ()
	Get a pointer to the input point cloud dataset target.
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 translation squared 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 translation squared difference between two consecutive transformations) as set by the user.
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.
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).
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.
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.
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).
bool	hasConverged () const
	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.
const std::string &	getClassName () const
	Abstract class get name method.
bool	initCompute ()
	Internal computation initialization.
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.
virtual	~PCLBase ()=default
	Destructor.
virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset.
PointCloudConstPtr const	getInputCloud () const
	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.
IndicesPtr	getIndices ()
	Get a pointer to the vector of indices used.
const PointSource &	operator[] (std::size_t pos) const
	Override PointCloud operator[] to shorten code.

Protected Member Functions
template<typename PointT>
void	computeCovariances (typename pcl::PointCloud< PointT >::ConstPtr cloud, const typename pcl::search::KdTree< PointT >::Ptr tree, MatricesVector &cloud_covariances)
	compute points covariances matrices according to the K nearest neighbors.
double	matricesInnerProd (const Eigen::MatrixXd &mat1, const Eigen::MatrixXd &mat2) const
void	computeTransformation (PointCloudSource &output, const Matrix4 &guess) override
	Rigid transformation computation method with initial guess.
bool	searchForNeighbors (const PointSource &query, pcl::Indices &index, std::vector< float > &distance)
	Search for the closest nearest neighbor of a given point.
void	applyState (Matrix4 &t, const Vector6d &x) const
	compute transformation matrix from transformation matrix
Protected Member Functions inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
virtual void	transformCloud (const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
	Apply a rigid transform to a given dataset.
void	computeTransformation (PointCloudSource &output, const Matrix4 &guess) override
	Rigid transformation computation method with initial guess.
virtual void	determineRequiredBlobData ()
	Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be called.
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.
virtual void	computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0
	Abstract transformation computation method with initial guess.
Protected Member Functions inherited from pcl::PCLBase< PointSource >
bool	initCompute ()
	This method should get called before starting the actual computation.
bool	deinitCompute ()
	This method should get called after finishing the actual computation.

Protected Attributes
int	k_correspondences_ {20}
	The number of neighbors used for covariances computation.
double	gicp_epsilon_ {0.001}
	The epsilon constant for gicp paper; this is NOT the convergence tolerance default: 0.001.
double	rotation_epsilon_ {2e-3}
	The epsilon constant for rotation error.
Matrix4	base_transformation_
	base transformation
const PointCloudSource *	tmp_src_
	Temporary pointer to the source dataset.
const PointCloudTarget *	tmp_tgt_
	Temporary pointer to the target dataset.
const pcl::Indices *	tmp_idx_src_
	Temporary pointer to the source dataset indices.
const pcl::Indices *	tmp_idx_tgt_
	Temporary pointer to the target dataset indices.
MatricesVectorPtr	input_covariances_
	Input cloud points covariances.
MatricesVectorPtr	target_covariances_
	Target cloud points covariances.
std::vector< Eigen::Matrix3d >	mahalanobis_
	Mahalanobis matrices holder.
int	max_inner_iterations_ {20}
	maximum number of optimizations
double	translation_gradient_tolerance_ {1e-2}
	minimal translation gradient for early optimization stop
double	rotation_gradient_tolerance_ {1e-2}
	minimal rotation gradient for early optimization stop
std::function< void(const pcl::PointCloud< PointSource > &cloud_src, const pcl::Indices &src_indices, const pcl::PointCloud< PointTarget > &cloud_tgt, const pcl::Indices &tgt_indices, Matrix4 &transformation_matrix)>	rigid_transformation_estimation_
Protected Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
std::size_t	x_idx_offset_
	XYZ fields offset.
std::size_t	y_idx_offset_
std::size_t	z_idx_offset_
std::size_t	nx_idx_offset_
	Normal fields offset.
std::size_t	ny_idx_offset_
std::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.
bool	need_source_blob_
	Checks for whether estimators and rejectors need various data.
bool	need_target_blob_
Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, float >
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	transformation_rotation_epsilon_
	The maximum rotation 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.
unsigned 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.
std::function< UpdateVisualizerCallbackSignature >	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.

Additional Inherited Members
Public Attributes inherited from pcl::IterativeClosestPoint< PointSource, PointTarget, float >
pcl::registration::DefaultConvergenceCriteria< float >::Ptr	convergence_criteria_

Detailed Description

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

GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al.

in http://www.robots.ox.ac.uk/~avsegal/resources/papers/Generalized_ICP.pdf The approach is based on using anisotropic cost functions to optimize the alignment after closest point assignments have been made. The original code uses GSL and ANN while in ours we use FLANN and Newton's method for optimization (call useBFGS to switch to BFGS optimizer, however Newton is usually faster and more accurate). Basic usage example:

pcl::GeneralizedIterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> reg;
reg.setInputSource(src);
reg.setInputTarget(tgt);
// use default parameters or set them yourself, for example:
// reg.setMaximumIterations(...);
// reg.setTransformationEpsilon(...);
// reg.setRotationEpsilon(...);
// reg.setCorrespondenceRandomness(...);
pcl::PointCloud<pcl::PointXYZ>::Ptr output(new pcl::PointCloud<pcl::PointXYZ>);
// supply a better guess, if possible:
Eigen::Matrix4f guess = Eigen::Matrix4f::Identity();
reg.align(*output, guess);
std::cout << reg.getFinalTransformation() << std::endl;

Author: Nizar Sallem

Definition at line 75 of file gicp.h.

Member Typedef Documentation

◆ AngleAxis

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::AngleAxis = typename Eigen::AngleAxis<Scalar>

Definition at line 132 of file gicp.h.

◆ ConstPtr

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::ConstPtr

Initial value:

shared_ptr<

const GeneralizedIterativeClosestPoint<PointSource, PointTarget, Scalar>>

pcl::GeneralizedIterativeClosestPoint::GeneralizedIterativeClosestPoint

PCL_MAKE_ALIGNED_OPERATOR_NEW GeneralizedIterativeClosestPoint()

Empty constructor.

Definition gicp.h:137

Definition at line 122 of file gicp.h.

◆ InputKdTree

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::InputKdTree = typename Registration<PointSource, PointTarget, Scalar>::KdTree

Definition at line 116 of file gicp.h.

◆ InputKdTreePtr

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::InputKdTreePtr

Initial value:

typename Registration<PointSource, PointTarget, Scalar>::KdTreePtr

pcl::Registration::KdTreePtr

typename KdTree::Ptr KdTreePtr

Definition registration.h:71

Definition at line 117 of file gicp.h.

◆ MatricesVector

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::MatricesVector

Initial value:

std::vector<Eigen::Matrix3d, Eigen::aligned_allocator<Eigen::Matrix3d>>

Definition at line 111 of file gicp.h.

◆ MatricesVectorConstPtr

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::MatricesVectorConstPtr = shared_ptr<const MatricesVector>

Definition at line 114 of file gicp.h.

◆ MatricesVectorPtr

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::MatricesVectorPtr = shared_ptr<MatricesVector>

Definition at line 113 of file gicp.h.

◆ Matrix3

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix3 = typename Eigen::Matrix<Scalar, 3, 3>

Definition at line 128 of file gicp.h.

◆ Matrix4

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4

Initial value:

typename IterativeClosestPoint<PointSource, PointTarget, Scalar>::Matrix4

pcl::IterativeClosestPoint::Matrix4

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

Definition icp.h:143

Definition at line 129 of file gicp.h.

◆ Matrix6d

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix6d = Eigen::Matrix<double, 6, 6>

Definition at line 131 of file gicp.h.

◆ PointCloudSource

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource = pcl::PointCloud<PointSource>

Definition at line 100 of file gicp.h.

◆ PointCloudSourceConstPtr

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

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

Definition at line 102 of file gicp.h.

◆ PointCloudSourcePtr

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

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

Definition at line 101 of file gicp.h.

◆ PointCloudTarget

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudTarget = pcl::PointCloud<PointTarget>

Definition at line 104 of file gicp.h.

◆ PointCloudTargetConstPtr

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

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

Definition at line 106 of file gicp.h.

◆ PointCloudTargetPtr

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

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

Definition at line 105 of file gicp.h.

◆ PointIndicesConstPtr

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

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

Definition at line 109 of file gicp.h.

◆ PointIndicesPtr

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

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

Definition at line 108 of file gicp.h.

◆ Ptr

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Ptr

Initial value:

shared_ptr<GeneralizedIterativeClosestPoint<PointSource, PointTarget, Scalar>>

Definition at line 120 of file gicp.h.

◆ Vector3

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Vector3 = typename Eigen::Matrix<Scalar, 3, 1>

Definition at line 125 of file gicp.h.

◆ Vector4

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Vector4 = typename Eigen::Matrix<Scalar, 4, 1>

Definition at line 126 of file gicp.h.

◆ Vector6d

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

using pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::Vector6d = Eigen::Matrix<double, 6, 1>

Definition at line 127 of file gicp.h.

Constructor & Destructor Documentation

◆ GeneralizedIterativeClosestPoint()

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

PCL_MAKE_ALIGNED_OPERATOR_NEW pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::GeneralizedIterativeClosestPoint ( )

inline

Empty constructor.

Definition at line 137 of file gicp.h.

References pcl::Registration< PointSource, PointTarget, float >::corr_dist_threshold_, estimateRigidTransformationNewton(), mahalanobis_, pcl::Registration< PointSource, PointTarget, float >::max_iterations_, pcl::Registration< PointSource, PointTarget, float >::min_number_correspondences_, pcl::Registration< PointSource, PointTarget, float >::reg_name_, rigid_transformation_estimation_, setNumberOfThreads(), and pcl::Registration< PointSource, PointTarget, float >::transformation_epsilon_.

Referenced by pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::OptimizationFunctorWithIndices::OptimizationFunctorWithIndices().

Member Function Documentation

◆ applyState()

template<typename PointSource, typename PointTarget, typename Scalar>

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::applyState	(	Matrix4 &	t,
		const Vector6d &	x ) const

protected

compute transformation matrix from transformation matrix

Definition at line 918 of file gicp.hpp.

Referenced by estimateRigidTransformationBFGS().

◆ computeCovariances()

template<typename PointSource, typename PointTarget, typename Scalar>

template<typename PointT>

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeCovariances	(	typename pcl::PointCloud< PointT >::ConstPtr	cloud,
		const typename pcl::search::KdTree< PointT >::Ptr	tree,
		MatricesVector &	cloud_covariances )

protected

compute points covariances matrices according to the K nearest neighbors.

K is set via setCorrespondenceRandomness() method.

Parameters

[in]	cloud	pointer to point cloud
[in]	tree	KD tree performer for nearest neighbors search
[out]	cloud_covariances	covariances matrices for each point in the cloud

Definition at line 73 of file gicp.hpp.

References gicp_epsilon_, k_correspondences_, pcl::search::KdTree< PointT, Tree >::nearestKSearch(), and pcl::PointCloud< PointT >::size().

Referenced by computeTransformation().

◆ computeRDerivative()

template<typename PointSource, typename PointTarget, typename Scalar>

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::computeRDerivative	(	const Vector6d &	x,
		const Eigen::Matrix3d &	dCost_dR_T,
		Vector6d &	g ) const

Computes the derivative of the cost function w.r.t rotation angles.

rotation matrix is obtainded from rotation angles x[3], x[4] and x[5]

Returns: d/d_Phi, d/d_Theta, d/d_Psi respectively in g[3], g[4] and g[5]

Parameters

[in]	x	array representing 3D transformation
[in]	dCost_dR_T	the transpose of the derivative of the cost function w.r.t rotation matrix
[out]	g	gradient vector

Definition at line 205 of file gicp.hpp.

◆ computeTransformation()

template<typename PointSource, typename PointTarget, typename Scalar>

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

inlineoverrideprotected

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 769 of file gicp.hpp.

◆ estimateRigidTransformationBFGS()

template<typename PointSource, typename PointTarget, typename Scalar>

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::estimateRigidTransformationBFGS	(	const PointCloudSource &	cloud_src,
		const pcl::Indices &	indices_src,
		const PointCloudTarget &	cloud_tgt,
		const pcl::Indices &	indices_tgt,
		Matrix4 &	transformation_matrix )

Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear BFGS approach.

Parameters

[in]	cloud_src	the source point cloud dataset
[in]	indices_src	the vector of indices describing the points of interest in cloud_src
[in]	cloud_tgt	the target point cloud dataset
[in]	indices_tgt	the vector of indices describing the correspondences of the interest points from indices_src
[in,out]	transformation_matrix	the resultant transformation matrix

Definition at line 296 of file gicp.hpp.

Referenced by useBFGS().

◆ estimateRigidTransformationNewton()

template<typename PointSource, typename PointTarget, typename Scalar>

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::estimateRigidTransformationNewton	(	const PointCloudSource &	cloud_src,
		const pcl::Indices &	indices_src,
		const PointCloudTarget &	cloud_tgt,
		const pcl::Indices &	indices_tgt,
		Matrix4 &	transformation_matrix )

Estimate a rigid rotation transformation between a source and a target point cloud using an iterative non-linear Newton approach.

Parameters

[in]	cloud_src	the source point cloud dataset
[in]	indices_src	the vector of indices describing the points of interest in cloud_src
[in]	cloud_tgt	the target point cloud dataset
[in]	indices_tgt	the vector of indices describing the correspondences of the interest points from indices_src
[in,out]	transformation_matrix	the resultant transformation matrix

Definition at line 372 of file gicp.hpp.

Referenced by GeneralizedIterativeClosestPoint().

◆ getCorrespondenceRandomness()

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

int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getCorrespondenceRandomness ( ) const

inline

Get the number of neighbors used when computing covariances as set by the user.

Definition at line 303 of file gicp.h.

References k_correspondences_.

◆ getMaximumOptimizerIterations()

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

int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getMaximumOptimizerIterations ( ) const

inline

Return maximum number of iterations at the optimization step.

Definition at line 335 of file gicp.h.

References max_inner_iterations_.

◆ getRotationEpsilon()

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getRotationEpsilon ( ) const

inline

Get the rotation epsilon (maximum allowable difference between two consecutive rotations) as set by the user.

Definition at line 282 of file gicp.h.

References rotation_epsilon_.

◆ getRotationGradientTolerance()

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getRotationGradientTolerance ( ) const

inline

Return the minimal rotation gradient threshold for early optimization stop.

Definition at line 370 of file gicp.h.

References rotation_gradient_tolerance_.

◆ getTranslationGradientTolerance()

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::getTranslationGradientTolerance ( ) const

inline

Return the minimal translation gradient threshold for early optimization stop.

Definition at line 353 of file gicp.h.

References translation_gradient_tolerance_.

◆ mahalanobis()

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

const Eigen::Matrix3d & pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::mahalanobis ( std::size_t index ) const

inline

Returns: Mahalanobis distance matrix for the given point index

Definition at line 248 of file gicp.h.

References mahalanobis_.

◆ matricesInnerProd()

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::matricesInnerProd	(	const Eigen::MatrixXd &	mat1,
		const Eigen::MatrixXd &	mat2 ) const

inlineprotected

Returns: trace of mat1 . mat2

Parameters

mat1	matrix of dimension nxm
mat2	matrix of dimension mxp

Definition at line 450 of file gicp.h.

◆ searchForNeighbors()

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

bool pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::searchForNeighbors	(	const PointSource &	query,
		pcl::Indices &	index,
		std::vector< float > &	distance )

inlineprotected

Search for the closest nearest neighbor of a given point.

Parameters

query	the point to search a nearest neighbour for
index	vector of size 1 to store the index of the nearest neighbour found
distance	vector of size 1 to store the distance to nearest neighbour found

Definition at line 476 of file gicp.h.

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

◆ setCorrespondenceRandomness()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setCorrespondenceRandomness ( int k )

inline

Set the number of neighbors used when selecting a point neighbourhood to compute covariances.

A higher value will bring more accurate covariance matrix but will make covariances computation slower.

Parameters

k	the number of neighbors to use when computing covariances

Definition at line 294 of file gicp.h.

References k_correspondences_.

◆ setInputSource()

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

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

inlineoverridevirtual

Provide a pointer to the input dataset.

Parameters

cloud the const boost shared pointer to a PointCloud message

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

Definition at line 159 of file gicp.h.

References pcl::Registration< PointSource, PointTarget, float >::getClassName(), input_covariances_, pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputSource(), and pcl::PointCloud< PointT >::size().

◆ setInputTarget()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setInputTarget ( const PointCloudTargetConstPtr & target )

inlineoverride

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

Parameters

[in] target the input point cloud target

Definition at line 193 of file gicp.h.

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

◆ setMaximumOptimizerIterations()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setMaximumOptimizerIterations ( int max )

inline

Set maximum number of iterations at the optimization step.

Parameters

[in] max maximum number of iterations for the optimizer

Definition at line 327 of file gicp.h.

References max_inner_iterations_.

◆ setNumberOfThreads()

template<typename PointSource, typename PointTarget, typename Scalar>

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setNumberOfThreads ( unsigned int nr_threads = 0 )

Initialize the scheduler and 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 50 of file gicp.hpp.

Referenced by GeneralizedIterativeClosestPoint().

◆ setRotationEpsilon()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setRotationEpsilon ( double epsilon )

inline

Set the rotation epsilon (maximum allowable difference between two consecutive rotations) in order for an optimization to be considered as having converged to the final solution.

Parameters

epsilon the rotation epsilon

Definition at line 273 of file gicp.h.

References rotation_epsilon_.

◆ setRotationGradientTolerance()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setRotationGradientTolerance ( double tolerance )

inline

Set the minimal rotation gradient threshold for early optimization stop.

Parameters

[in] tolerance rotation gradient threshold in radians

Definition at line 362 of file gicp.h.

References rotation_gradient_tolerance_.

◆ setSourceCovariances()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setSourceCovariances ( const MatricesVectorPtr & covariances )

inline

Provide a pointer to the covariances of the input source (if computed externally!).

If not set, GeneralizedIterativeClosestPoint will compute the covariances itself. Make sure to set the covariances AFTER setting the input source point cloud (setting the input source point cloud will reset the covariances).

Parameters

[in] covariances the input source covariances

Definition at line 184 of file gicp.h.

References input_covariances_.

◆ setTargetCovariances()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setTargetCovariances ( const MatricesVectorPtr & covariances )

inline

Provide a pointer to the covariances of the input target (if computed externally!).

If not set, GeneralizedIterativeClosestPoint will compute the covariances itself. Make sure to set the covariances AFTER setting the input source point cloud (setting the input source point cloud will reset the covariances).

Parameters

[in] covariances the input target covariances

Definition at line 207 of file gicp.h.

References target_covariances_.

◆ setTranslationGradientTolerance()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::setTranslationGradientTolerance ( double tolerance )

inline

Set the minimal translation gradient threshold for early optimization stop.

Parameters

[in] tolerance translation gradient threshold in meters

Definition at line 344 of file gicp.h.

References translation_gradient_tolerance_.

◆ useBFGS()

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

void pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::useBFGS ( )

inline

Use BFGS optimizer instead of default Newton optimizer.

Definition at line 311 of file gicp.h.

References estimateRigidTransformationBFGS(), and rigid_transformation_estimation_.

Member Data Documentation

◆ base_transformation_

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

Matrix4 pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::base_transformation_

protected

base transformation

Definition at line 401 of file gicp.h.

Referenced by computeTransformation().

◆ gicp_epsilon_

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::gicp_epsilon_ {0.001}

protected

The epsilon constant for gicp paper; this is NOT the convergence tolerance default: 0.001.

Definition at line 392 of file gicp.h.

Referenced by computeCovariances().

◆ input_covariances_

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

MatricesVectorPtr pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::input_covariances_

protected

Input cloud points covariances.

Definition at line 416 of file gicp.h.

Referenced by computeTransformation(), setInputSource(), and setSourceCovariances().

◆ k_correspondences_

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

int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::k_correspondences_ {20}

protected

The number of neighbors used for covariances computation.

default: 20

Definition at line 386 of file gicp.h.

Referenced by computeCovariances(), getCorrespondenceRandomness(), and setCorrespondenceRandomness().

◆ mahalanobis_

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

std::vector<Eigen::Matrix3d> pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::mahalanobis_

protected

Mahalanobis matrices holder.

Definition at line 422 of file gicp.h.

Referenced by computeTransformation(), GeneralizedIterativeClosestPoint(), and mahalanobis().

◆ max_inner_iterations_

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

int pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::max_inner_iterations_ {20}

protected

maximum number of optimizations

Definition at line 425 of file gicp.h.

Referenced by estimateRigidTransformationBFGS(), getMaximumOptimizerIterations(), and setMaximumOptimizerIterations().

◆ rigid_transformation_estimation_

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

std::function<void(const pcl::PointCloud<PointSource>& cloud_src, const pcl::Indices& src_indices, const pcl::PointCloud<PointTarget>& cloud_tgt, const pcl::Indices& tgt_indices, Matrix4& transformation_matrix)> pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rigid_transformation_estimation_

protected

Definition at line 514 of file gicp.h.

Referenced by computeTransformation(), GeneralizedIterativeClosestPoint(), and useBFGS().

◆ rotation_epsilon_

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_epsilon_ {2e-3}

protected

The epsilon constant for rotation error.

(In GICP the transformation epsilon is split in rotation part and translation part). default: 2e-3

Definition at line 398 of file gicp.h.

Referenced by computeTransformation(), getRotationEpsilon(), and setRotationEpsilon().

◆ rotation_gradient_tolerance_

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::rotation_gradient_tolerance_ {1e-2}

protected

minimal rotation gradient for early optimization stop

Definition at line 431 of file gicp.h.

Referenced by getRotationGradientTolerance(), and setRotationGradientTolerance().

◆ target_covariances_

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

MatricesVectorPtr pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::target_covariances_

protected

Target cloud points covariances.

Definition at line 419 of file gicp.h.

Referenced by computeTransformation(), setInputTarget(), and setTargetCovariances().

◆ tmp_idx_src_

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

const pcl::Indices* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_idx_src_

protected

Temporary pointer to the source dataset indices.

Definition at line 410 of file gicp.h.

Referenced by estimateRigidTransformationBFGS().

◆ tmp_idx_tgt_

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

const pcl::Indices* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_idx_tgt_

protected

Temporary pointer to the target dataset indices.

Definition at line 413 of file gicp.h.

Referenced by estimateRigidTransformationBFGS().

◆ tmp_src_

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

const PointCloudSource* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_src_

protected

Temporary pointer to the source dataset.

Definition at line 404 of file gicp.h.

Referenced by estimateRigidTransformationBFGS().

◆ tmp_tgt_

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

const PointCloudTarget* pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::tmp_tgt_

protected

Temporary pointer to the target dataset.

Definition at line 407 of file gicp.h.

Referenced by estimateRigidTransformationBFGS().

◆ translation_gradient_tolerance_

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

double pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::translation_gradient_tolerance_ {1e-2}

protected

minimal translation gradient for early optimization stop

Definition at line 428 of file gicp.h.

Referenced by getTranslationGradientTolerance(), and setTranslationGradientTolerance().

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

pcl/registration/gicp.h
pcl/registration/impl/gicp.hpp

Classes

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ AngleAxis

◆ ConstPtr

◆ InputKdTree

◆ InputKdTreePtr

◆ MatricesVector

◆ MatricesVectorConstPtr

◆ MatricesVectorPtr

◆ Matrix3

◆ Matrix4

◆ Matrix6d

◆ PointCloudSource

◆ PointCloudSourceConstPtr

◆ PointCloudSourcePtr

◆ PointCloudTarget

◆ PointCloudTargetConstPtr

◆ PointCloudTargetPtr

◆ PointIndicesConstPtr

◆ PointIndicesPtr

◆ Ptr

◆ Vector3

◆ Vector4

◆ Vector6d

Constructor & Destructor Documentation

◆ GeneralizedIterativeClosestPoint()

Member Function Documentation

◆ applyState()

◆ computeCovariances()

◆ computeRDerivative()

◆ computeTransformation()

◆ estimateRigidTransformationBFGS()

◆ estimateRigidTransformationNewton()

◆ getCorrespondenceRandomness()

◆ getMaximumOptimizerIterations()

◆ getRotationEpsilon()

◆ getRotationGradientTolerance()

◆ getTranslationGradientTolerance()

◆ mahalanobis()

◆ matricesInnerProd()

◆ searchForNeighbors()

◆ setCorrespondenceRandomness()

◆ setInputSource()

◆ setInputTarget()

◆ setMaximumOptimizerIterations()

◆ setNumberOfThreads()

◆ setRotationEpsilon()

◆ setRotationGradientTolerance()

◆ setSourceCovariances()

◆ setTargetCovariances()

◆ setTranslationGradientTolerance()

◆ useBFGS()

Member Data Documentation

◆ base_transformation_

◆ gicp_epsilon_

◆ input_covariances_

◆ k_correspondences_

◆ mahalanobis_

◆ max_inner_iterations_

◆ rigid_transformation_estimation_

◆ rotation_epsilon_

◆ rotation_gradient_tolerance_

◆ target_covariances_

◆ tmp_idx_src_

◆ tmp_idx_tgt_

◆ tmp_src_

◆ tmp_tgt_

◆ translation_gradient_tolerance_