NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching. More...

#include <pcl/registration/ndt_2d.h>

Inheritance diagram for pcl::NormalDistributionsTransform2D< PointSource, PointTarget >:

Public Types
using	Ptr = shared_ptr<NormalDistributionsTransform2D<PointSource, PointTarget>>
using	ConstPtr
Public Types inherited from pcl::Registration< PointSource, PointTarget, Scalar >
using	Matrix4 = Eigen::Matrix<Scalar, 4, 4>
using	Ptr = shared_ptr<Registration<PointSource, PointTarget, Scalar>>
using	ConstPtr = shared_ptr<const Registration<PointSource, PointTarget, Scalar>>
using	CorrespondenceRejectorPtr = pcl::registration::CorrespondenceRejector::Ptr
using	KdTree = pcl::search::KdTree<PointTarget>
using	KdTreePtr = typename KdTree::Ptr
using	KdTreeReciprocal = pcl::search::KdTree<PointSource>
using	KdTreeReciprocalPtr = typename KdTreeReciprocal::Ptr
using	PointCloudSource = pcl::PointCloud<PointSource>
using	PointCloudSourcePtr = typename PointCloudSource::Ptr
using	PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr
using	PointCloudTarget = pcl::PointCloud<PointTarget>
using	PointCloudTargetPtr = typename PointCloudTarget::Ptr
using	PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr
using	PointRepresentationConstPtr = typename KdTree::PointRepresentationConstPtr
using	TransformationEstimation
using	TransformationEstimationPtr = typename TransformationEstimation::Ptr
using	TransformationEstimationConstPtr = typename TransformationEstimation::ConstPtr
using	CorrespondenceEstimation
using	CorrespondenceEstimationPtr = typename CorrespondenceEstimation::Ptr
using	CorrespondenceEstimationConstPtr = typename CorrespondenceEstimation::ConstPtr
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
	NormalDistributionsTransform2D ()
	Empty constructor.
	~NormalDistributionsTransform2D () override=default
	Empty destructor.
virtual void	setGridCentre (const Eigen::Vector2f &centre)
	centre of the ndt grid (target coordinate system)
virtual void	setGridStep (const Eigen::Vector2f &step)
	Grid spacing (step) of the NDT grid.
virtual void	setGridExtent (const Eigen::Vector2f &extent)
	NDT Grid extent (in either direction from the grid centre).
virtual void	setOptimizationStepSize (const double &lambda)
	NDT Newton optimisation step size parameter.
virtual void	setOptimizationStepSize (const Eigen::Vector3d &lambda)
	NDT Newton optimisation step size parameter.
Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
	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.
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 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).
double	getFitnessScore (const std::vector< float > &distances_a, const std::vector< float > &distances_b)
	Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) from two sets of correspondence distances (distances between source and target points).
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.
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 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
void	computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess) override
	Rigid transformation computation method with initial guess.
Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, Scalar >
bool	searchForNeighbors (const PointCloudSource &cloud, int index, pcl::Indices &indices, std::vector< float > &distances)
	Search for the closest nearest neighbor of a given point.
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
Eigen::Vector2f	grid_centre_
Eigen::Vector2f	grid_step_
Eigen::Vector2f	grid_extent_
Eigen::Vector3d	newton_lambda_
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_ {0}
	The number of iterations the internal optimization ran for (used internally).
int	max_iterations_ {10}
	The maximum number of iterations the internal optimization should run for.
int	ransac_iterations_ {0}
	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_ {0.0}
	The maximum difference between two consecutive transformations in order to consider convergence (user defined).
double	transformation_rotation_epsilon_ {0.0}
	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_ {0.05}
	The inlier distance threshold for the internal RANSAC outlier rejection loop.
bool	converged_ {false}
	Holds internal convergence state, given user parameters.
unsigned int	min_number_correspondences_ {3}
	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_ {true}
	Variable that stores whether we have a new target cloud, meaning we need to pre-process it again.
bool	source_cloud_updated_ {true}
	Variable that stores whether we have a new source cloud, meaning we need to pre-process it again.
bool	force_no_recompute_ {false}
	A flag which, if set, means the tree operating on the target cloud will never be recomputed.
bool	force_no_recompute_reciprocal_ {false}
	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.

Detailed Description

template<typename PointSource, typename PointTarget>
class pcl::NormalDistributionsTransform2D< PointSource, PointTarget >

NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching.

This implementation is intended to match the definition: Peter Biber and Wolfgang Straßer. The normal distributions transform: A new approach to laser scan matching. In Proceedings of the IEEE In- ternational Conference on Intelligent Robots and Systems (IROS), pages 2743–2748, Las Vegas, USA, October 2003.

Author: James Crosby

Definition at line 60 of file ndt_2d.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointSource, typename PointTarget>

using pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::ConstPtr

Initial value:

shared_ptr<const NormalDistributionsTransform2D<PointSource, PointTarget>>

Definition at line 74 of file ndt_2d.h.

◆ Ptr

template<typename PointSource, typename PointTarget>

using pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::Ptr = shared_ptr<NormalDistributionsTransform2D<PointSource, PointTarget>>

Definition at line 73 of file ndt_2d.h.

Constructor & Destructor Documentation

◆ NormalDistributionsTransform2D()

template<typename PointSource, typename PointTarget>

pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::NormalDistributionsTransform2D ( )

inline

Empty constructor.

Definition at line 78 of file ndt_2d.h.

References grid_centre_, grid_extent_, grid_step_, newton_lambda_, pcl::Registration< PointSource, PointTarget, Scalar >::reg_name_, and pcl::Registration< PointSource, PointTarget, Scalar >::Registration().

◆ ~NormalDistributionsTransform2D()

template<typename PointSource, typename PointTarget>

pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::~NormalDistributionsTransform2D ( )

overridedefault

Empty destructor.

Member Function Documentation

◆ computeTransformation()

template<typename PointSource, typename PointTarget>

void pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::computeTransformation	(	PointCloudSource &	output,
		const Eigen::Matrix4f &	guess )

overrideprotected

Rigid transformation computation method with initial guess.

Parameters

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

Definition at line 392 of file ndt_2d.hpp.

◆ setGridCentre()

template<typename PointSource, typename PointTarget>

virtual void pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::setGridCentre ( const Eigen::Vector2f & centre )

inlinevirtual

centre of the ndt grid (target coordinate system)

Parameters

centre value to set

Definition at line 95 of file ndt_2d.h.

References grid_centre_.

◆ setGridExtent()

template<typename PointSource, typename PointTarget>

virtual void pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::setGridExtent ( const Eigen::Vector2f & extent )

inlinevirtual

NDT Grid extent (in either direction from the grid centre).

Parameters

[in] extent value to set

Definition at line 113 of file ndt_2d.h.

References grid_extent_.

◆ setGridStep()

template<typename PointSource, typename PointTarget>

virtual void pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::setGridStep ( const Eigen::Vector2f & step )

inlinevirtual

Grid spacing (step) of the NDT grid.

Parameters

[in] step value to set

Definition at line 104 of file ndt_2d.h.

References grid_step_.

◆ setOptimizationStepSize() [1/2]

template<typename PointSource, typename PointTarget>

virtual void pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::setOptimizationStepSize ( const double & lambda )

inlinevirtual

NDT Newton optimisation step size parameter.

Parameters

[in] lambda step size: 1 is simple newton optimisation, smaller values may improve convergence

Definition at line 123 of file ndt_2d.h.

References newton_lambda_.

◆ setOptimizationStepSize() [2/2]

template<typename PointSource, typename PointTarget>

virtual void pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::setOptimizationStepSize ( const Eigen::Vector3d & lambda )

inlinevirtual

NDT Newton optimisation step size parameter.

Parameters

[in] lambda step size: (1,1,1) is simple newton optimisation, smaller values may improve convergence, or elements may be set to zero to prevent optimisation over some parameters

This overload allows control of updates to the individual (x, y, theta) free parameters in the optimisation. If, for example, theta is believed to be close to the correct value a small value of lambda[2] should be used.

Definition at line 139 of file ndt_2d.h.

References newton_lambda_.

Member Data Documentation

◆ grid_centre_

template<typename PointSource, typename PointTarget>

Eigen::Vector2f pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::grid_centre_

protected

Definition at line 167 of file ndt_2d.h.

Referenced by computeTransformation(), NormalDistributionsTransform2D(), and setGridCentre().

◆ grid_extent_

template<typename PointSource, typename PointTarget>

Eigen::Vector2f pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::grid_extent_

protected

Definition at line 169 of file ndt_2d.h.

Referenced by computeTransformation(), NormalDistributionsTransform2D(), and setGridExtent().

◆ grid_step_

template<typename PointSource, typename PointTarget>

Eigen::Vector2f pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::grid_step_

protected

Definition at line 168 of file ndt_2d.h.

Referenced by computeTransformation(), NormalDistributionsTransform2D(), and setGridStep().

◆ newton_lambda_

template<typename PointSource, typename PointTarget>

Eigen::Vector3d pcl::NormalDistributionsTransform2D< PointSource, PointTarget >::newton_lambda_

protected

Definition at line 170 of file ndt_2d.h.

Referenced by computeTransformation(), NormalDistributionsTransform2D(), setOptimizationStepSize(), and setOptimizationStepSize().

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

pcl/registration/ndt_2d.h
pcl/registration/impl/ndt_2d.hpp

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ ConstPtr

◆ Ptr

Constructor & Destructor Documentation

◆ NormalDistributionsTransform2D()

◆ ~NormalDistributionsTransform2D()

Member Function Documentation

◆ computeTransformation()

◆ setGridCentre()

◆ setGridExtent()

◆ setGridStep()

◆ setOptimizationStepSize() [1/2]

◆ setOptimizationStepSize() [2/2]

Member Data Documentation

◆ grid_centre_

◆ grid_extent_

◆ grid_step_

◆ newton_lambda_