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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

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...

Functions

template<typename PointT >
void pcl::getApproximateIndices (const typename pcl::PointCloud< PointT >::ConstPtr &cloud_in, const typename pcl::PointCloud< PointT >::ConstPtr &cloud_ref, Indices &indices)
Get a set of approximate indices for a given point cloud into a reference point cloud. More...
template<typename Point1T , typename Point2T >
void pcl::getApproximateIndices (const typename pcl::PointCloud< Point1T >::ConstPtr &cloud_in, const typename pcl::PointCloud< Point2T >::ConstPtr &cloud_ref, Indices &indices)
Get a set of approximate indices for a given point cloud into a reference point cloud. More...

Function Documentation

getApproximateIndices() [1/2]

template<typename Point1T , typename Point2T >
void pcl::getApproximateIndices ( const typename pcl::PointCloud< Point1T >::ConstPtr & cloud_in,
const typename pcl::PointCloud< Point2T >::ConstPtr & cloud_ref,
Indices & indices
)

#include <pcl/kdtree/io.h>

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 47 of file io.hpp.

References pcl::KdTree< PointT >::nearestKSearchT(), pcl::KdTreeFLANN< PointT, Dist >::setInputCloud(), and pcl::PointCloud< PointT >::size().

getApproximateIndices() [2/2]

template<typename PointT >
void pcl::getApproximateIndices ( const typename pcl::PointCloud< PointT >::ConstPtr & cloud_in,
const typename pcl::PointCloud< PointT >::ConstPtr & cloud_ref,
Indices & indices
)

#include <pcl/kdtree/io.h>

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 67 of file io.hpp.

References pcl::KdTreeFLANN< PointT, Dist >::nearestKSearch(), pcl::KdTreeFLANN< PointT, Dist >::setInputCloud(), and pcl::PointCloud< PointT >::size().

© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/group__kdtree.html