A 3D Normal Distribution Transform registration implementation for point cloud data. More...

#include <pcl/registration/ndt.h>

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

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

Public Types
using	Ptr = shared_ptr< NormalDistributionsTransform< PointSource, PointTarget, Scalar > >

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

using	Vector3 = typename Eigen::Matrix< Scalar, 3, 1 >

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

using	Affine3 = typename Eigen::Transform< Scalar, 3, Eigen::Affine >

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

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

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

using	CorrespondenceRejectorPtr = pcl::registration::CorrespondenceRejector::Ptr

using	KdTree = pcl::search::KdTree< PointTarget >

using	KdTreePtr = typename KdTree::Ptr

using	KdTreeReciprocal = pcl::search::KdTree< PointSource >

using	KdTreeReciprocalPtr = typename KdTreeReciprocal::Ptr

using	PointCloudSource = pcl::PointCloud< PointSource >

using	PointCloudSourcePtr = typename PointCloudSource::Ptr

using	PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

using	PointCloudTarget = pcl::PointCloud< PointTarget >

using	PointCloudTargetPtr = typename PointCloudTarget::Ptr

using	PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr

using	PointRepresentationConstPtr = typename KdTree::PointRepresentationConstPtr

using	TransformationEstimation = typename pcl::registration::TransformationEstimation< PointSource, PointTarget, float >

using	TransformationEstimationPtr = typename TransformationEstimation::Ptr

using	TransformationEstimationConstPtr = typename TransformationEstimation::ConstPtr

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

using	CorrespondenceEstimationPtr = typename CorrespondenceEstimation::Ptr

using	CorrespondenceEstimationConstPtr = typename CorrespondenceEstimation::ConstPtr

using	UpdateVisualizerCallbackSignature = void(const pcl::PointCloud< PointSource > &, const pcl::Indices &, const pcl::PointCloud< PointTarget > &, const pcl::Indices &)
	The callback signature to the function updating intermediate source point cloud position during it's registration to the target point cloud. More...

Public Types inherited from pcl::PCLBase< PointSource >
using	PointCloud = pcl::PointCloud< PointSource >

using	PointCloudPtr = typename PointCloud::Ptr

using	PointCloudConstPtr = typename PointCloud::ConstPtr

using	PointIndicesPtr = PointIndices::Ptr

using	PointIndicesConstPtr = PointIndices::ConstPtr

Public Member Functions
	NormalDistributionsTransform ()
	Constructor. More...

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

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

void	setResolution (float resolution)
	Set/change the voxel grid resolution. More...

void	setMinPointPerVoxel (unsigned int min_points_per_voxel)
	Set the minimum number of points required for a cell to be used (must be 3 or greater for covariance calculation). More...

float	getResolution () const
	Get voxel grid resolution. More...

double	getStepSize () const
	Get the newton line search maximum step length. More...

void	setStepSize (double step_size)
	Set/change the newton line search maximum step length. More...

double	getOulierRatio () const
	Get the point cloud outlier ratio. More...

void	setOulierRatio (double outlier_ratio)
	Set/change the point cloud outlier ratio. More...

double	getTransformationLikelihood () const
	Get the registration alignment likelihood. More...

double	getTransformationProbability () const
	Get the registration alignment probability. More...

int	getFinalNumIteration () const
	Get the number of iterations required to calculate alignment. More...

const TargetGrid &	getTargetCells () const
	Get access to the `VoxelGridCovariance` generated from target cloud containing point means and covariances. More...

Public Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
	Registration ()
	Empty constructor. More...

	~Registration () override=default
	destructor. More...

void	setTransformationEstimation (const TransformationEstimationPtr &te)
	Provide a pointer to the transformation estimation object. More...

void	setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce)
	Provide a pointer to the correspondence estimation object. More...

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...

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

PointCloudTargetConstPtr const	getInputTarget ()
	Get a pointer to the input point cloud dataset target. More...

void	setSearchMethodTarget (const KdTreePtr &tree, bool force_no_recompute=false)
	Provide a pointer to the search object used to find correspondences in the target cloud. More...

KdTreePtr	getSearchMethodTarget () const
	Get a pointer to the search method used to find correspondences in the target cloud. More...

void	setSearchMethodSource (const KdTreeReciprocalPtr &tree, bool force_no_recompute=false)
	Provide a pointer to the search object used to find correspondences in the source cloud (usually used by reciprocal correspondence finding). More...

KdTreeReciprocalPtr	getSearchMethodSource () const
	Get a pointer to the search method used to find correspondences in the source cloud. More...

Matrix4	getFinalTransformation ()
	Get the final transformation matrix estimated by the registration method. More...

Matrix4	getLastIncrementalTransformation ()
	Get the last incremental transformation matrix estimated by the registration method. More...

void	setMaximumIterations (int nr_iterations)
	Set the maximum number of iterations the internal optimization should run for. More...

int	getMaximumIterations ()
	Get the maximum number of iterations the internal optimization should run for, as set by the user. More...

void	setRANSACIterations (int ransac_iterations)
	Set the number of iterations RANSAC should run for. More...

double	getRANSACIterations ()
	Get the number of iterations RANSAC should run for, as set by the user. More...

void	setRANSACOutlierRejectionThreshold (double inlier_threshold)
	Set the inlier distance threshold for the internal RANSAC outlier rejection loop. More...

double	getRANSACOutlierRejectionThreshold ()
	Get the inlier distance threshold for the internal outlier rejection loop as set by the user. More...

void	setMaxCorrespondenceDistance (double distance_threshold)
	Set the maximum distance threshold between two correspondent points in source <-> target. More...

double	getMaxCorrespondenceDistance ()
	Get the maximum distance threshold between two correspondent points in source <-> target. More...

void	setTransformationEpsilon (double epsilon)
	Set the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More...

double	getTransformationEpsilon ()
	Get the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) as set by the user. More...

void	setTransformationRotationEpsilon (double epsilon)
	Set the transformation rotation epsilon (maximum allowable rotation difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More...

double	getTransformationRotationEpsilon ()
	Get the transformation rotation epsilon (maximum allowable difference between two consecutive transformations) as set by the user (epsilon is the cos(angle) in a axis-angle representation). More...

void	setEuclideanFitnessEpsilon (double epsilon)
	Set the maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More...

double	getEuclideanFitnessEpsilon ()
	Get the maximum allowed distance error before the algorithm will be considered to have converged, as set by the user. More...

void	setPointRepresentation (const PointRepresentationConstPtr &point_representation)
	Provide a boost shared pointer to the PointRepresentation to be used when comparing points. More...

bool	registerVisualizationCallback (std::function< UpdateVisualizerCallbackSignature > &visualizerCallback)
	Register the user callback function which will be called from registration thread in order to update point cloud obtained after each iteration. More...

double	getFitnessScore (double max_range=std::numeric_limits< double >::max())
	Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) More...

double	getFitnessScore (const std::vector< float > &distances_a, const std::vector< float > &distances_b)
	Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) from two sets of correspondence distances (distances between source and target points) More...

bool	hasConverged () const
	Return the state of convergence after the last align run. More...

void	align (PointCloudSource &output)
	Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More...

void	align (PointCloudSource &output, const Matrix4 &guess)
	Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More...

const std::string &	getClassName () const
	Abstract class get name method. More...

bool	initCompute ()
	Internal computation initialization. More...

bool	initComputeReciprocal ()
	Internal computation when reciprocal lookup is needed. More...

void	addCorrespondenceRejector (const CorrespondenceRejectorPtr &rejector)
	Add a new correspondence rejector to the list. More...

std::vector< CorrespondenceRejectorPtr >	getCorrespondenceRejectors ()
	Get the list of correspondence rejectors. More...

bool	removeCorrespondenceRejector (unsigned int i)
	Remove the i-th correspondence rejector in the list. More...

void	clearCorrespondenceRejectors ()
	Clear the list of correspondence rejectors. More...

Public Member Functions inherited from pcl::PCLBase< PointSource >
	PCLBase ()
	Empty constructor. More...

	PCLBase (const PCLBase &base)
	Copy constructor. More...

virtual	~PCLBase ()=default
	Destructor. More...

virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset. More...

PointCloudConstPtr const	getInputCloud () const
	Get a pointer to the input point cloud dataset. More...

virtual void	setIndices (const IndicesPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (const IndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (const PointIndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols)
	Set the indices for the points laying within an interest region of the point cloud. More...

IndicesPtr	getIndices ()
	Get a pointer to the vector of indices used. More...

IndicesConstPtr const	getIndices () const
	Get a pointer to the vector of indices used. More...

const PointSource &	operator[] (std::size_t pos) const
	Override PointCloud operator[] to shorten code. More...

Static Public Member Functions
static void	convertTransform (const Eigen::Matrix< double, 6, 1 > &x, Affine3 &trans)
	Convert 6 element transformation vector to affine transformation. More...

static void	convertTransform (const Eigen::Matrix< double, 6, 1 > &x, Matrix4 &trans)
	Convert 6 element transformation vector to transformation matrix. More...

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

using	PointCloudSourcePtr = typename PointCloudSource::Ptr

using	PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr

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

using	PointCloudTargetPtr = typename PointCloudTarget::Ptr

using	PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr

using	PointIndicesPtr = PointIndices::Ptr

using	PointIndicesConstPtr = PointIndices::ConstPtr

using	TargetGrid = VoxelGridCovariance< PointTarget >
	Typename of searchable voxel grid containing mean and covariance. More...

using	TargetGridPtr = TargetGrid *
	Typename of pointer to searchable voxel grid. More...

using	TargetGridConstPtr = const TargetGrid *
	Typename of const pointer to searchable voxel grid. More...

using	TargetGridLeafConstPtr = typename TargetGrid::LeafConstPtr
	Typename of const pointer to searchable voxel grid leaf. More...

Protected Member Functions
virtual void	computeTransformation (PointCloudSource &output)
	Estimate the transformation and returns the transformed source (input) as output. More...

void	computeTransformation (PointCloudSource &output, const Matrix4 &guess) override
	Estimate the transformation and returns the transformed source (input) as output. More...

void	init ()
	Initiate covariance voxel structure. More...

double	computeDerivatives (Eigen::Matrix< double, 6, 1 > &score_gradient, Eigen::Matrix< double, 6, 6 > &hessian, const PointCloudSource &trans_cloud, const Eigen::Matrix< double, 6, 1 > &transform, bool compute_hessian=true)
	Compute derivatives of likelihood function w.r.t. More...

double	updateDerivatives (Eigen::Matrix< double, 6, 1 > &score_gradient, Eigen::Matrix< double, 6, 6 > &hessian, const Eigen::Vector3d &x_trans, const Eigen::Matrix3d &c_inv, bool compute_hessian=true) const
	Compute individual point contributions to derivatives of likelihood function w.r.t. More...

void	computeAngleDerivatives (const Eigen::Matrix< double, 6, 1 > &transform, bool compute_hessian=true)
	Precompute angular components of derivatives. More...

void	computePointDerivatives (const Eigen::Vector3d &x, bool compute_hessian=true)
	Compute point derivatives. More...

void	computeHessian (Eigen::Matrix< double, 6, 6 > &hessian, const PointCloudSource &trans_cloud)
	Compute hessian of likelihood function w.r.t. More...

void	computeHessian (Eigen::Matrix< double, 6, 6 > &hessian, const PointCloudSource &trans_cloud, const Eigen::Matrix< double, 6, 1 > &transform)
	Compute hessian of likelihood function w.r.t. More...

void	updateHessian (Eigen::Matrix< double, 6, 6 > &hessian, const Eigen::Vector3d &x_trans, const Eigen::Matrix3d &c_inv) const
	Compute individual point contributions to hessian of likelihood function w.r.t. More...

double	computeStepLengthMT (const Eigen::Matrix< double, 6, 1 > &transform, Eigen::Matrix< double, 6, 1 > &step_dir, double step_init, double step_max, double step_min, double &score, Eigen::Matrix< double, 6, 1 > &score_gradient, Eigen::Matrix< double, 6, 6 > &hessian, PointCloudSource &trans_cloud)
	Compute line search step length and update transform and likelihood derivatives using More-Thuente method. More...

bool	updateIntervalMT (double &a_l, double &f_l, double &g_l, double &a_u, double &f_u, double &g_u, double a_t, double f_t, double g_t) const
	Update interval of possible step lengths for More-Thuente method, in More-Thuente (1994) More...

double	trialValueSelectionMT (double a_l, double f_l, double g_l, double a_u, double f_u, double g_u, double a_t, double f_t, double g_t) const
	Select new trial value for More-Thuente method. More...

double	auxilaryFunction_PsiMT (double a, double f_a, double f_0, double g_0, double mu=1.e-4) const
	Auxiliary function used to determine endpoints of More-Thuente interval. More...

double	auxilaryFunction_dPsiMT (double g_a, double g_0, double mu=1.e-4) const
	Auxiliary function derivative used to determine endpoints of More-Thuente interval. More...

Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget, float >
bool	searchForNeighbors (const PointCloudSource &cloud, int index, pcl::Indices &indices, std::vector< float > &distances)
	Search for the closest nearest neighbor of a given point. More...

virtual void	computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0
	Abstract transformation computation method with initial guess. More...

Protected Member Functions inherited from pcl::PCLBase< PointSource >
bool	initCompute ()
	This method should get called before starting the actual computation. More...

bool	deinitCompute ()
	This method should get called after finishing the actual computation. More...

Protected Attributes
TargetGrid	target_cells_
	The voxel grid generated from target cloud containing point means and covariances. More...

float	resolution_ {1.0f}
	The side length of voxels. More...

double	step_size_ {0.1}
	The maximum step length. More...

double	outlier_ratio_ {0.55}
	The ratio of outliers of points w.r.t. More...

double	gauss_d1_ {0.0}
	The normalization constants used fit the point distribution to a normal distribution, Equation 6.8 [Magnusson 2009]. More...

double	gauss_d2_ {0.0}

union {
double trans_probability_

double trans_likelihood_ {0.0}

};
	The likelihood score of the transform applied to the input cloud, Equation 6.9 and 6.10 [Magnusson 2009]. More...

Eigen::Matrix< double, 8, 4 >	angular_jacobian_
	Precomputed Angular Gradient. More...

Eigen::Matrix< double, 15, 4 >	angular_hessian_
	Precomputed Angular Hessian. More...

Eigen::Matrix< double, 3, 6 >	point_jacobian_
	The first order derivative of the transformation of a point w.r.t. More...

Eigen::Matrix< double, 18, 6 >	point_hessian_
	The second order derivative of the transformation of a point w.r.t. More...

Protected Attributes inherited from pcl::Registration< PointSource, PointTarget, float >
std::string	reg_name_
	The registration method name. More...

KdTreePtr	tree_
	A pointer to the spatial search object. More...

KdTreeReciprocalPtr	tree_reciprocal_
	A pointer to the spatial search object of the source. More...

int	nr_iterations_
	The number of iterations the internal optimization ran for (used internally). More...

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

int	ransac_iterations_
	The number of iterations RANSAC should run for. More...

PointCloudTargetConstPtr	target_
	The input point cloud dataset target. More...

Matrix4	final_transformation_
	The final transformation matrix estimated by the registration method after N iterations. More...

Matrix4	transformation_
	The transformation matrix estimated by the registration method. More...

Matrix4	previous_transformation_
	The previous transformation matrix estimated by the registration method (used internally). More...

double	transformation_epsilon_
	The maximum difference between two consecutive transformations in order to consider convergence (user defined). More...

double	transformation_rotation_epsilon_
	The maximum rotation difference between two consecutive transformations in order to consider convergence (user defined). More...

double	euclidean_fitness_epsilon_
	The maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More...

double	corr_dist_threshold_
	The maximum distance threshold between two correspondent points in source <-> target. More...

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

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

unsigned int	min_number_correspondences_
	The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More...

CorrespondencesPtr	correspondences_
	The set of correspondences determined at this ICP step. More...

TransformationEstimationPtr	transformation_estimation_
	A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More...

CorrespondenceEstimationPtr	correspondence_estimation_
	A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More...

std::vector< CorrespondenceRejectorPtr >	correspondence_rejectors_
	The list of correspondence rejectors to use. More...

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

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

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

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

std::function< UpdateVisualizerCallbackSignature >	update_visualizer_
	Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More...

Protected Attributes inherited from pcl::PCLBase< PointSource >
PointCloudConstPtr	input_
	The input point cloud dataset. More...

IndicesPtr	indices_
	A pointer to the vector of point indices to use. More...

bool	use_indices_
	Set to true if point indices are used. More...

bool	fake_indices_
	If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More...

Detailed Description

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

A 3D Normal Distribution Transform registration implementation for point cloud data.

Note: For more information please see Magnusson, M. (2009). The Three-Dimensional Normal-Distributions Transform — an Efﬁcient Representation for Registration, Surface Analysis, and Loop Detection. PhD thesis, Orebro University. Orebro Studies in Technology 36., More, J., and Thuente, D. (1994). Line Search Algorithm with Guaranteed Sufficient Decrease In ACM Transactions on Mathematical Software. and Sun, W. and Yuan, Y, (2006) Optimization Theory and Methods: Nonlinear Programming. 89-100; Math refactored by Todor Stoyanov.

Author: Brian Okorn (Space and Naval Warfare Systems Center Pacific)

Definition at line 66 of file ndt.h.

Member Typedef Documentation

◆ Affine3

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

using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::Affine3 = typename Eigen::Transform<Scalar, 3, Eigen::Affine>

Definition at line 99 of file ndt.h.

◆ ConstPtr

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

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

Definition at line 95 of file ndt.h.

◆ Matrix4

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

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

Definition at line 98 of file ndt.h.

◆ PointCloudSource

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

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

protected

Definition at line 69 of file ndt.h.

◆ PointCloudSourceConstPtr

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

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

protected

Definition at line 72 of file ndt.h.

◆ PointCloudSourcePtr

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

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

protected

Definition at line 71 of file ndt.h.

◆ PointCloudTarget

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

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

protected

Definition at line 74 of file ndt.h.

◆ PointCloudTargetConstPtr

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

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

protected

Definition at line 77 of file ndt.h.

◆ PointCloudTargetPtr

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

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

protected

Definition at line 76 of file ndt.h.

◆ PointIndicesConstPtr

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

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

protected

Definition at line 80 of file ndt.h.

◆ PointIndicesPtr

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

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

protected

Definition at line 79 of file ndt.h.

◆ Ptr

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

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

Definition at line 93 of file ndt.h.

◆ TargetGrid

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

using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::TargetGrid = VoxelGridCovariance<PointTarget>

protected

Typename of searchable voxel grid containing mean and covariance.

Definition at line 84 of file ndt.h.

◆ TargetGridConstPtr

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

using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::TargetGridConstPtr = const TargetGrid*

protected

Typename of const pointer to searchable voxel grid.

Definition at line 88 of file ndt.h.

◆ TargetGridLeafConstPtr

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

using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::TargetGridLeafConstPtr = typename TargetGrid::LeafConstPtr

protected

Typename of const pointer to searchable voxel grid leaf.

Definition at line 90 of file ndt.h.

◆ TargetGridPtr

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

using pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::TargetGridPtr = TargetGrid*

protected

Typename of pointer to searchable voxel grid.

Definition at line 86 of file ndt.h.

◆ Vector3

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

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

Definition at line 97 of file ndt.h.

Constructor & Destructor Documentation

◆ NormalDistributionsTransform()

template<typename PointSource , typename PointTarget , typename Scalar >

pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform

Constructor.

Sets outlier_ratio_ to 0.55, step_size_ to 0.1 and resolution_ to 1.0

Definition at line 47 of file ndt.hpp.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::gauss_d1_, pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::gauss_d2_, pcl::Registration< PointSource, PointTarget, float >::max_iterations_, pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_, pcl::Registration< PointSource, PointTarget, float >::reg_name_, pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_, and pcl::Registration< PointSource, PointTarget, float >::transformation_epsilon_.

◆ ~NormalDistributionsTransform()

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

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

overridedefault

Empty destructor.

Member Function Documentation

◆ auxilaryFunction_dPsiMT()

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::auxilaryFunction_dPsiMT	(	double	g_a,
		double	g_0,
		double	mu = `1.e-4`
	)		const

inlineprotected

Auxiliary function derivative used to determine endpoints of More-Thuente interval.

Note: $\psi'(\alpha)$ , derivative of Equation 1.6 (Moore, Thuente 1994)

Parameters

[in]	g_a	function gradient at step length a, $\phi'(\alpha)$ in More-Thuente (1994)
[in]	g_0	initial function gradient, $\phi'(0)$ in More-Thuente (1994)
[in]	mu	the step length, constant $\mu$ in Equation 1.1 [More, Thuente 1994]

Returns: sufficient decrease derivative

Definition at line 568 of file ndt.h.

◆ auxilaryFunction_PsiMT()

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::auxilaryFunction_PsiMT	(	double	a,
		double	f_a,
		double	f_0,
		double	g_0,
		double	mu = `1.e-4`
	)		const

inlineprotected

Auxiliary function used to determine endpoints of More-Thuente interval.

Note: $\psi(\alpha)$ in Equation 1.6 (Moore, Thuente 1994)

Parameters

[in]	a	the step length, $\alpha$ in More-Thuente (1994)
[in]	f_a	function value at step length a, $\phi(\alpha)$ in More-Thuente (1994)
[in]	f_0	initial function value, $\phi(0)$ in Moore-Thuente (1994)
[in]	g_0	initial function gradient, $\phi'(0)$ in More-Thuente (1994)
[in]	mu	the step length, constant $\mu$ in Equation 1.1 [More, Thuente 1994]

Returns: sufficient decrease value

Definition at line 549 of file ndt.h.

◆ computeAngleDerivatives()

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeAngleDerivatives	(	const Eigen::Matrix< double, 6, 1 > &	transform,
		bool	compute_hessian = `true`
	)

protected

Precompute angular components of derivatives.

Note: Equation 6.19 and 6.21 [Magnusson 2009].

Parameters

[in]	transform	the current transform vector
[in]	compute_hessian	flag to calculate hessian, unnecessary for step calculation.

Definition at line 236 of file ndt.hpp.

◆ computeDerivatives()

template<typename PointSource , typename PointTarget , typename Scalar >

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeDerivatives	(	Eigen::Matrix< double, 6, 1 > &	score_gradient,
		Eigen::Matrix< double, 6, 6 > &	hessian,
		const PointCloudSource &	trans_cloud,
		const Eigen::Matrix< double, 6, 1 > &	transform,
		bool	compute_hessian = `true`
	)

protected

Compute derivatives of likelihood function w.r.t.

the transformation vector.

Note: Equation 6.10, 6.12 and 6.13 [Magnusson 2009].

Parameters

[out]	score_gradient	the gradient vector of the likelihood function w.r.t. the transformation vector
[out]	hessian	the hessian matrix of the likelihood function w.r.t. the transformation vector
[in]	trans_cloud	transformed point cloud
[in]	transform	the current transform vector
[in]	compute_hessian	flag to calculate hessian, unnecessary for step calculation.

Definition at line 185 of file ndt.hpp.

◆ computeHessian() [1/2]

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeHessian	(	Eigen::Matrix< double, 6, 6 > &	hessian,
		const PointCloudSource &	trans_cloud
	)

protected

Compute hessian of likelihood function w.r.t.

the transformation vector.

Note: Equation 6.13 [Magnusson 2009].

Parameters

[out]	hessian	the hessian matrix of the likelihood function w.r.t. the transformation vector
[in]	trans_cloud	transformed point cloud

Definition at line 415 of file ndt.hpp.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeHessian().

◆ computeHessian() [2/2]

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

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeHessian	(	Eigen::Matrix< double, 6, 6 > &	hessian,
		const PointCloudSource &	trans_cloud,
		const Eigen::Matrix< double, 6, 1 > &	transform
	)

inlineprotected

Compute hessian of likelihood function w.r.t.

the transformation vector.

Note: Equation 6.13 [Magnusson 2009].

Parameters

[out]	hessian	the hessian matrix of the likelihood function w.r.t. the transformation vector
[in]	trans_cloud	transformed point cloud
[in]	transform	the current transform vector

Deprecated:: Scheduled for removal in version 1 . 15 : "Parameter `transform` is not required"

Definition at line 394 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeHessian(), and pcl::utils::ignore().

◆ computePointDerivatives()

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computePointDerivatives	(	const Eigen::Vector3d &	x,
		bool	compute_hessian = `true`
	)

protected

Compute point derivatives.

Note: Equation 6.18-21 [Magnusson 2009].

Parameters

[in]	x	point from the input cloud
[in]	compute_hessian	flag to calculate hessian, unnecessary for step calculation.

Definition at line 321 of file ndt.hpp.

◆ computeStepLengthMT()

template<typename PointSource , typename PointTarget , typename Scalar >

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeStepLengthMT	(	const Eigen::Matrix< double, 6, 1 > &	transform,
		Eigen::Matrix< double, 6, 1 > &	step_dir,
		double	step_init,
		double	step_max,
		double	step_min,
		double &	score,
		Eigen::Matrix< double, 6, 1 > &	score_gradient,
		Eigen::Matrix< double, 6, 6 > &	hessian,
		PointCloudSource &	trans_cloud
	)

protected

Compute line search step length and update transform and likelihood derivatives using More-Thuente method.

Note: Search Algorithm [More, Thuente 1994]

Parameters

[in]	transform	initial transformation vector, in Equation 1.3 (Moore, Thuente 1994) and $\vec{p}$ in Algorithm 2 [Magnusson 2009]
[in]	step_dir	descent direction, in Equation 1.3 (Moore, Thuente 1994) and $\delta \vec{p}$ normalized in Algorithm 2 [Magnusson 2009]
[in]	step_init	initial step length estimate, $\alpha_0$ in Moore-Thuente (1994) and the normal of $\delta \vec{p}$ in Algorithm 2 [Magnusson 2009]
[in]	step_max	maximum step length, $\alpha_max$ in Moore-Thuente (1994)
[in]	step_min	minimum step length, $\alpha_min$ in Moore-Thuente (1994)
[out]	score	final score function value, $f(x + \alpha p)$ in Equation 1.3 (Moore, Thuente 1994) and in Algorithm 2 [Magnusson 2009]
[in,out]	score_gradient	gradient of score function w.r.t. transformation vector, $f'(x + \alpha p)$ in Moore-Thuente (1994) and $\vec{g}$ in Algorithm 2 [Magnusson 2009]
[out]	hessian	hessian of score function w.r.t. transformation vector, $f''(x + \alpha p)$ in Moore-Thuente (1994) and in Algorithm 2 [Magnusson 2009]
[in,out]	trans_cloud	transformed point cloud, transformed by $T(\vec{p},\vec{x})$ in Algorithm 2 [Magnusson 2009]

Returns: final step length

Definition at line 650 of file ndt.hpp.

References pcl::transformPointCloud().

◆ computeTransformation() [1/2]

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

virtual void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::computeTransformation ( PointCloudSource & output )

inlineprotectedvirtual

Estimate the transformation and returns the transformed source (input) as output.

Parameters

[out] output the resultant input transformed point cloud dataset

Definition at line 289 of file ndt.h.

◆ computeTransformation() [2/2]

template<typename PointSource , typename PointTarget , typename Scalar >

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

overrideprotected

Estimate the transformation and returns the transformed source (input) as output.

Parameters

[out]	output	the resultant input transformed point cloud dataset
[in]	guess	the initial gross estimation of the transformation

Definition at line 68 of file ndt.hpp.

References pcl::transformPointCloud().

◆ convertTransform() [1/2]

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

static void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::convertTransform	(	const Eigen::Matrix< double, 6, 1 > &	x,
		Affine3 &	trans
	)

inlinestatic

Convert 6 element transformation vector to affine transformation.

Parameters

[in]	x	transformation vector of the form [x, y, z, roll, pitch, yaw]
[out]	trans	affine transform corresponding to given transformation vector

Definition at line 243 of file ndt.h.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::convertTransform().

◆ convertTransform() [2/2]

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

static void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::convertTransform	(	const Eigen::Matrix< double, 6, 1 > &	x,
		Matrix4 &	trans
	)

inlinestatic

Convert 6 element transformation vector to transformation matrix.

Parameters

[in]	x	transformation vector of the form [x, y, z, roll, pitch, yaw]
[out]	trans	4x4 transformation matrix corresponding to given transformation vector

Definition at line 257 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::convertTransform().

◆ getFinalNumIteration()

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

int pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getFinalNumIteration ( ) const

inline

Get the number of iterations required to calculate alignment.

Returns: final number of iterations

Definition at line 218 of file ndt.h.

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

◆ getOulierRatio()

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getOulierRatio ( ) const

inline

Get the point cloud outlier ratio.

Returns: outlier ratio

Definition at line 178 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_.

◆ getResolution()

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

float pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getResolution ( ) const

inline

Get voxel grid resolution.

Returns: side length of voxels

Definition at line 151 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_.

◆ getStepSize()

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getStepSize ( ) const

inline

Get the newton line search maximum step length.

Returns: maximum step length

Definition at line 160 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::step_size_.

◆ getTargetCells()

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

const TargetGrid& pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTargetCells ( ) const

inline

Get access to the VoxelGridCovariance generated from target cloud containing point means and covariances.

Set the input target cloud before calling this. Useful for debugging, e.g.

pcl::PointCloud<PointXYZ> visualize_cloud;

ndt.getTargetCells().getDisplayCloud(visualize_cloud);

pcl::PointCloud

PointCloud represents the base class in PCL for storing collections of 3D points.

Definition: point_cloud.h:173

Definition at line 232 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_.

◆ getTransformationLikelihood()

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTransformationLikelihood ( ) const

inline

Get the registration alignment likelihood.

Returns: transformation likelihood

Definition at line 196 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_likelihood_.

◆ getTransformationProbability()

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTransformationProbability ( ) const

inline

Get the registration alignment probability.

Returns: transformation probability

Deprecated:: Scheduled for removal in version 1 . 16 : "The method `getTransformationProbability` has been renamed to " "`getTransformationLikelihood`."

Definition at line 209 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_likelihood_.

◆ init()

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

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init ( )

inlineprotected

Initiate covariance voxel structure.

Definition at line 303 of file ndt.h.

References pcl::VoxelGridCovariance< PointT >::filter(), pcl::VoxelGridCovariance< PointT >::getCentroids(), pcl::Registration< PointSource, PointTarget, float >::getClassName(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_, pcl::PCLBase< PointT >::setInputCloud(), pcl::VoxelGrid< PointT >::setLeafSize(), pcl::Registration< PointSource, PointTarget, float >::target_, and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setInputTarget(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setResolution().

◆ setInputTarget()

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

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

inlineoverridevirtual

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

Parameters

[in] cloud the input point cloud target

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

Definition at line 114 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), and pcl::Registration< PointSource, PointTarget, Scalar >::setInputTarget().

◆ setMinPointPerVoxel()

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

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setMinPointPerVoxel ( unsigned int min_points_per_voxel )

inline

Set the minimum number of points required for a cell to be used (must be 3 or greater for covariance calculation).

Calls the function of the underlying VoxelGridCovariance. This function must be called before setInputTarget and setResolution.

Parameters

[in] min_points_per_voxel the minimum number of points required for a voxel to be used

Definition at line 142 of file ndt.h.

References pcl::VoxelGridCovariance< PointT >::setMinPointPerVoxel(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_.

◆ setOulierRatio()

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

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setOulierRatio ( double outlier_ratio )

inline

Set/change the point cloud outlier ratio.

Parameters

[in] outlier_ratio outlier ratio

Definition at line 187 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_.

◆ setResolution()

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

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setResolution ( float resolution )

inline

Set/change the voxel grid resolution.

Parameters

[in] resolution side length of voxels

Definition at line 124 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), pcl::PCLBase< PointSource >::input_, and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_.

◆ setStepSize()

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

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setStepSize ( double step_size )

inline

Set/change the newton line search maximum step length.

Parameters

[in] step_size maximum step length

Definition at line 169 of file ndt.h.

References pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::step_size_.

◆ trialValueSelectionMT()

template<typename PointSource , typename PointTarget , typename Scalar >

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trialValueSelectionMT	(	double	a_l,
		double	f_l,
		double	g_l,
		double	a_u,
		double	f_u,
		double	g_u,
		double	a_t,
		double	f_t,
		double	g_t
	)		const

protected

Select new trial value for More-Thuente method.

Note: Trial Value Selection [More, Thuente 1994], $\psi(\alpha_k)$ is used for and until some value satisfies the test $\psi(\alpha_k) \leq 0$ and $\phi'(\alpha_k) \geq 0$ then $\phi(\alpha_k)$ is used from then on.; Interpolation Minimizer equations from Optimization Theory and Methods: Nonlinear Programming By Wenyu Sun, Ya-xiang Yuan (89-100).

Parameters

[in]	a_l	first endpoint of interval , $\alpha_l$ in Moore-Thuente (1994)
[in]	f_l	value at first endpoint, in Moore-Thuente (1994)
[in]	g_l	derivative at first endpoint, in Moore-Thuente (1994)
[in]	a_u	second endpoint of interval , $\alpha_u$ in Moore-Thuente (1994)
[in]	f_u	value at second endpoint, in Moore-Thuente (1994)
[in]	g_u	derivative at second endpoint, in Moore-Thuente (1994)
[in]	a_t	previous trial value, $\alpha_t$ in Moore-Thuente (1994)
[in]	f_t	value at previous trial value, in Moore-Thuente (1994)
[in]	g_t	derivative at previous trial value, in Moore-Thuente (1994)

Returns: new trial value

Definition at line 537 of file ndt.hpp.

◆ updateDerivatives()

template<typename PointSource , typename PointTarget , typename Scalar >

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::updateDerivatives	(	Eigen::Matrix< double, 6, 1 > &	score_gradient,
		Eigen::Matrix< double, 6, 6 > &	hessian,
		const Eigen::Vector3d &	x_trans,
		const Eigen::Matrix3d &	c_inv,
		bool	compute_hessian = `true`
	)		const

protected

Compute individual point contributions to derivatives of likelihood function w.r.t.

the transformation vector.

Note: Equation 6.10, 6.12 and 6.13 [Magnusson 2009].

Parameters

[in,out]	score_gradient	the gradient vector of the likelihood function w.r.t. the transformation vector
[in,out]	hessian	the hessian matrix of the likelihood function w.r.t. the transformation vector
[in]	x_trans	transformed point minus mean of occupied covariance voxel
[in]	c_inv	covariance of occupied covariance voxel
[in]	compute_hessian	flag to calculate hessian, unnecessary for step calculation.

Definition at line 367 of file ndt.hpp.

◆ updateHessian()

template<typename PointSource , typename PointTarget , typename Scalar >

void pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::updateHessian	(	Eigen::Matrix< double, 6, 6 > &	hessian,
		const Eigen::Vector3d &	x_trans,
		const Eigen::Matrix3d &	c_inv
	)		const

protected

Compute individual point contributions to hessian of likelihood function w.r.t.

the transformation vector.

Note: Equation 6.13 [Magnusson 2009].

Parameters

[in,out]	hessian	the hessian matrix of the likelihood function w.r.t. the transformation vector
[in]	x_trans	transformed point minus mean of occupied covariance voxel
[in]	c_inv	covariance of occupied covariance voxel

Definition at line 457 of file ndt.hpp.

◆ updateIntervalMT()

template<typename PointSource , typename PointTarget , typename Scalar >

bool pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::updateIntervalMT	(	double &	a_l,
		double &	f_l,
		double &	g_l,
		double &	a_u,
		double &	f_u,
		double &	g_u,
		double	a_t,
		double	f_t,
		double	g_t
	)		const

protected

Update interval of possible step lengths for More-Thuente method, in More-Thuente (1994)

Note: Updating Algorithm until some value satisfies $\psi(\alpha_k) \leq 0$ and $\phi'(\alpha_k) \geq 0$ and Modified Updating Algorithm from then on [More, Thuente 1994].

Parameters

[in,out]	a_l	first endpoint of interval , $\alpha_l$ in Moore-Thuente (1994)
[in,out]	f_l	value at first endpoint, in Moore-Thuente (1994), $\psi(\alpha_l)$ for Update Algorithm and $\phi(\alpha_l)$ for Modified Update Algorithm
[in,out]	g_l	derivative at first endpoint, in Moore-Thuente (1994), $\psi'(\alpha_l)$ for Update Algorithm and $\phi'(\alpha_l)$ for Modified Update Algorithm
[in,out]	a_u	second endpoint of interval , $\alpha_u$ in Moore-Thuente (1994)
[in,out]	f_u	value at second endpoint, in Moore-Thuente (1994), $\psi(\alpha_u)$ for Update Algorithm and $\phi(\alpha_u)$ for Modified Update Algorithm
[in,out]	g_u	derivative at second endpoint, in Moore-Thuente (1994), $\psi'(\alpha_u)$ for Update Algorithm and $\phi'(\alpha_u)$ for Modified Update Algorithm
[in]	a_t	trial value, $\alpha_t$ in Moore-Thuente (1994)
[in]	f_t	value at trial value, in Moore-Thuente (1994), $\psi(\alpha_t)$ for Update Algorithm and $\phi(\alpha_t)$ for Modified Update Algorithm
[in]	g_t	derivative at trial value, in Moore-Thuente (1994), $\psi'(\alpha_t)$ for Update Algorithm and $\phi'(\alpha_t)$ for Modified Update Algorithm

Returns: if interval converges

Definition at line 492 of file ndt.hpp.

Member Data Documentation

◆

union { ... }

The likelihood score of the transform applied to the input cloud, Equation 6.9 and 6.10 [Magnusson 2009].

◆ angular_hessian_

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

Eigen::Matrix<double, 15, 4> pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::angular_hessian_

protected

Precomputed Angular Hessian.

The precomputed angular derivatives for the hessian of a transformation vector, Equation 6.19 [Magnusson 2009].

Definition at line 613 of file ndt.h.

◆ angular_jacobian_

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

Eigen::Matrix<double, 8, 4> pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::angular_jacobian_

protected

Precomputed Angular Gradient.

The precomputed angular derivatives for the jacobian of a transformation vector, Equation 6.19 [Magnusson 2009].

Definition at line 606 of file ndt.h.

◆ gauss_d1_

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::gauss_d1_ {0.0}

protected

The normalization constants used fit the point distribution to a normal distribution, Equation 6.8 [Magnusson 2009].

Definition at line 589 of file ndt.h.

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

◆ gauss_d2_

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::gauss_d2_ {0.0}

protected

Definition at line 589 of file ndt.h.

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

◆ outlier_ratio_

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::outlier_ratio_ {0.55}

protected

The ratio of outliers of points w.r.t.

a normal distribution, Equation 6.7 [Magnusson 2009].

Definition at line 585 of file ndt.h.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getOulierRatio(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setOulierRatio().

◆ point_hessian_

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

Eigen::Matrix<double, 18, 6> pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::point_hessian_

protected

The second order derivative of the transformation of a point w.r.t.

the transform vector, in Equation 6.20 [Magnusson 2009].

Definition at line 623 of file ndt.h.

◆ point_jacobian_

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

Eigen::Matrix<double, 3, 6> pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::point_jacobian_

protected

The first order derivative of the transformation of a point w.r.t.

the transform vector, in Equation 6.18 [Magnusson 2009].

Definition at line 618 of file ndt.h.

◆ resolution_

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

float pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::resolution_ {1.0f}

protected

The side length of voxels.

Definition at line 578 of file ndt.h.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getResolution(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::NormalDistributionsTransform(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setResolution().

◆ step_size_

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::step_size_ {0.1}

protected

The maximum step length.

Definition at line 581 of file ndt.h.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getStepSize(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setStepSize().

◆ target_cells_

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

TargetGrid pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::target_cells_

protected

The voxel grid generated from target cloud containing point means and covariances.

Definition at line 575 of file ndt.h.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTargetCells(), pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::init(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::setMinPointPerVoxel().

◆ trans_likelihood_

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_likelihood_ {0.0}

Definition at line 598 of file ndt.h.

Referenced by pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTransformationLikelihood(), and pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::getTransformationProbability().

◆ trans_probability_

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

double pcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >::trans_probability_

Deprecated:: Scheduled for removal in version 1 . 16 : "`trans_probability_` has been renamed to `trans_likelihood_`."

Definition at line 597 of file ndt.h.

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

pcl/registration/ndt.h
pcl/registration/impl/ndt.hpp

Public Types

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Detailed Description

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

Member Typedef Documentation

◆ Affine3

◆ ConstPtr

◆ Matrix4

◆ PointCloudSource

◆ PointCloudSourceConstPtr

◆ PointCloudSourcePtr

◆ PointCloudTarget

◆ PointCloudTargetConstPtr

◆ PointCloudTargetPtr

◆ PointIndicesConstPtr

◆ PointIndicesPtr

◆ Ptr

◆ TargetGrid

◆ TargetGridConstPtr

◆ TargetGridLeafConstPtr

◆ TargetGridPtr

◆ Vector3

Constructor & Destructor Documentation

◆ NormalDistributionsTransform()

◆ ~NormalDistributionsTransform()

Member Function Documentation

◆ auxilaryFunction_dPsiMT()

◆ auxilaryFunction_PsiMT()

◆ computeAngleDerivatives()

◆ computeDerivatives()

◆ computeHessian() [1/2]

◆ computeHessian() [2/2]

◆ computePointDerivatives()

◆ computeStepLengthMT()

◆ computeTransformation() [1/2]

◆ computeTransformation() [2/2]

◆ convertTransform() [1/2]

◆ convertTransform() [2/2]

◆ getFinalNumIteration()

◆ getOulierRatio()

◆ getResolution()

◆ getStepSize()

◆ getTargetCells()

◆ getTransformationLikelihood()

◆ getTransformationProbability()

◆ init()

◆ setInputTarget()

◆ setMinPointPerVoxel()

◆ setOulierRatio()

◆ setResolution()

◆ setStepSize()

◆ trialValueSelectionMT()

◆ updateDerivatives()

◆ updateHessian()

◆ updateIntervalMT()

Member Data Documentation

◆

◆ angular_hessian_

◆ angular_jacobian_

◆ gauss_d1_

◆ gauss_d2_

◆ outlier_ratio_

◆ point_hessian_

◆ point_jacobian_

◆ resolution_

◆ step_size_

◆ target_cells_

◆ trans_likelihood_

◆ trans_probability_

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