Module registration

---

Detailed Description

Overview

Combining several datasets into a global consistent model is usually performed using a technique called registration. The key idea is to identify corresponding points between the data sets and find a transformation that minimizes the distance (alignment error) between corresponding points. This process is repeated, since correspondence search is affected by the relative position and orientation of the data sets. Once the alignment errors fall below a given threshold, the registration is said to be complete.

The pcl_registration library implements a plethora of point cloud registration algorithms for both organized an unorganized (general purpose) datasets.

Requirements

• common
• kdtree
• sample_consensus
• features

Classes
class	pcl::registration::CorrespondenceEstimation< PointSource, PointTarget >
	CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features. More...
class	pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT >
	CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud More...
class	pcl::registration::CorrespondenceRejector
	CorrespondenceRejector represents the base class for correspondence rejection methods More...
class	pcl::registration::CorrespondenceRejectorDistance
	CorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences. More...
class	pcl::registration::CorrespondenceRejectorFeatures
	CorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors. More...
class	pcl::registration::CorrespondenceRejectorOneToOne
	CorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences. More...
class	pcl::registration::CorrespondenceRejectorSampleConsensus< PointT >
	CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers) More...
class	pcl::registration::CorrespondenceRejectorTrimmed
	CorrespondenceRejectorTrimmed implements a correspondence rejection for ICP-like registration algorithms that uses only the best 'k' correspondences where 'k' is some estimate of the overlap between the two point clouds being registered. More...
struct	pcl::registration::sortCorrespondencesByQueryIndex
	sortCorrespondencesByQueryIndex : a functor for sorting correspondences by query index More...
struct	pcl::registration::sortCorrespondencesByMatchIndex
	sortCorrespondencesByMatchIndex : a functor for sorting correspondences by match index More...
struct	pcl::registration::sortCorrespondencesByDistance
	sortCorrespondencesByDistance : a functor for sorting correspondences by distance More...
struct	pcl::registration::sortCorrespondencesByQueryIndexAndDistance
	sortCorrespondencesByQueryIndexAndDistance : a functor for sorting correspondences by query index and distance More...
struct	pcl::registration::sortCorrespondencesByMatchIndexAndDistance
	sortCorrespondencesByMatchIndexAndDistance : a functor for sorting correspondences by match index and distance More...
class	pcl::registration::ELCH< PointT >
	ELCH (Explicit Loop Closing Heuristic) class More...
class	pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >
	GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al. More...
class	pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >
	SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al. More...
class	pcl::IterativeClosestPoint< PointSource, PointTarget >
	IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm. More...
class	pcl::IterativeClosestPointNonLinear< PointSource, PointTarget >
	IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. More...
class	pcl::Registration< PointSource, PointTarget >
	Registration represents the base registration class. More...
class	pcl::registration::TransformationEstimation< PointSource, PointTarget >
	TransformationEstimation represents the base class for methods for transformation estimation based on: More...
class	pcl::registration::TransformationEstimationLM< PointSource, PointTarget >
	TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences. More...
class	pcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget >
	TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences. More...
class	pcl::registration::TransformationEstimationPointToPlaneLLS< PointSource, PointTarget >
	TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals. More...
class	pcl::registration::TransformationEstimationSVD< PointSource, PointTarget >
	TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences. More...
class	pcl::registration::TransformationValidation< PointSource, PointTarget >
	TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation. More...
class	pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget >
	TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset. More...