Detailed Description

Overview

The pcl_kdtree library provides the kd-tree data-structure, using FLANN, that allows for fast nearest neighbor searches.

A Kd-tree (k-dimensional tree) is a space-partitioning data structure that stores a set of k-dimensional points in a tree structure that enables efficient range searches and nearest neighbor searches. Nearest neighbor searches are a core operation when working with point cloud data and can be used to find correspondences between groups of points or feature descriptors or to define the local neighborhood around a point or points.

Requirements

common

Classes
class	pcl::KdTree< PointT >
	KdTree represents the base spatial locator class for kd-tree implementations. More...
class	pcl::KdTreeFLANN< PointT, Dist >
	KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More...
class	pcl::KdTreeFLANN< Eigen::MatrixXf >
	KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More...
Functions
template<typename PointT >
void	pcl::getApproximateIndices (const typename pcl::PointCloud< PointT >::Ptr &cloud_in, const typename pcl::PointCloud< PointT >::Ptr &cloud_ref, std::vector< int > &indices)
	Get a set of approximate indices for a given point cloud into a reference point cloud.
template<typename Point1T , typename Point2T >
void	pcl::getApproximateIndices (const typename pcl::PointCloud< Point1T >::Ptr &cloud_in, const typename pcl::PointCloud< Point2T >::Ptr &cloud_ref, std::vector< int > &indices)
	Get a set of approximate indices for a given point cloud into a reference point cloud.

Function Documentation

template<typename PointT >

void pcl::getApproximateIndices	(	const typename pcl::PointCloud< PointT >::Ptr &	cloud_in,
		const typename pcl::PointCloud< PointT >::Ptr &	cloud_ref,
		std::vector< int > &	indices
	)

Get a set of approximate indices for a given point cloud into a reference point cloud.

The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	cloud_ref	the reference point cloud dataset
[out]	indices	the resultant set of nearest neighbor indices of cloud_in in cloud_ref

Definition at line 68 of file io.hpp.

template<typename Point1T , typename Point2T >

void pcl::getApproximateIndices	(	const typename pcl::PointCloud< Point1T >::Ptr &	cloud_in,
		const typename pcl::PointCloud< Point2T >::Ptr &	cloud_ref,
		std::vector< int > &	indices
	)