Classes | Functions

Module kdtree


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.

kdtree_mug.png

History

Requirements

Classes

class  pcl::KdTree< PointT >
 KdTree represents the base spatial locator class for nearest neighbor estimation. More...
class  pcl::KdTreeFLANN< PointT >
 KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. More...
class  pcl::OrganizedDataIndex< PointT >
 OrganizedDataIndex is a type of spatial locator used to query organized datasets, such as point clouds acquired using dense stereo devices. More...

Functions

template<typename PointT >
void pcl::initTree (const int &spatial_locator, boost::shared_ptr< pcl::KdTree< PointT > > &tree, int k=0)
 Initialize the spatial locator used for nearest neighbor search.

Function Documentation

template<typename PointT >
void pcl::initTree ( const int &  spatial_locator,
boost::shared_ptr< pcl::KdTree< PointT > > &  tree,
int  k = 0 
)

Initialize the spatial locator used for nearest neighbor search.

Parameters:
spatial_locator the type of spatial locator to construct (0 = FLANN, 1 = organized)
tree the resultant tree as a boost shared pointer
k optional parameter (default 0) applied only if the spatial locator is set to organized (1)

Definition at line 47 of file tree_types.hpp.