Point Cloud Library (PCL)
1.3.1
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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.
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... |