Point Cloud Library (PCL)  1.3.1
Classes
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.

registration_outdoor.png
registration_closeup.png

History

Requirements

Classes

class  pcl::registration::CorrespondenceEstimation
 CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features. 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
 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
 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
 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
 IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm. More...
class  pcl::IterativeClosestPointNonLinear
 IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. More...
class  pcl::Registration
 Registration represents the base registration class. More...
class  pcl::registration::TransformationEstimation
 TransformationEstimation represents the base class for methods for transformation estimation based on: More...
class  pcl::registration::TransformationEstimationLM
 TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences. More...
class  pcl::registration::TransformationEstimationPointToPlane
 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
 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
 TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences. More...
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