Only released in EOL distros:
Package Summary
FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB and Python.
- Author: Marius Muja and David Lowe
- License: LGPL
- External website: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
- Repository: pointclouds
- Source: svn http://svn.pointclouds.org/ros/tags/perception_pcl-0.10.0
Package Summary
FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB and Python.
- Author: Marius Muja and David Lowe
- License: LGPL
- External website: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
- Source: svn http://svn.pointclouds.org/ros/branches/electric/perception_pcl
perception_pcl: flann | pcl | pcl_ros
Package Summary
FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB and Python.
- Author: Marius Muja and David Lowe
- License: LGPL
- External website: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
- Source: hg https://bitbucket.org/macmason/perception_pcl_fuerte_unstable (branch: default)
Package Summary
FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.
- Maintainer: Vincent Rabaud <vrabaud AT willowgarage DOT com>
- Author: Marius Muja <mariusm AT cs.ubc DOT ca>
- License: BSD
External Documentation
This is a third party package with external documentation.
