Only released in EOL distros:
Package Summary
PCL - Point Cloud Library: a comprehensive open source library for n-D Point Clouds and 3D geometry processing. The library contains numerous state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation, etc.
- Author: See http://pcl.ros.org/authors for the complete list of authors.
- License: BSD
- External website: http://pointclouds.org
- Repository: pointclouds
- Source: svn http://svn.pointclouds.org/ros/tags/perception_pcl-0.10.0
Package Summary
PCL - Point Cloud Library: a comprehensive open source library for n-D Point Clouds and 3D geometry processing. The library contains numerous state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation, etc.
- Author: See http://pcl.ros.org/authors for the complete list of authors.
- License: BSD
- External website: http://pointclouds.org
- Source: svn http://svn.pointclouds.org/ros/branches/electric/perception_pcl
Package Summary
PCL - Point Cloud Library: a comprehensive open source library for n-D Point Clouds and 3D geometry processing. The library contains numerous state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation, etc.
- Author: See http://pcl.ros.org/authors for the complete list of authors.
- License: BSD
- External website: http://pointclouds.org
- Source: svn http://svn.pointclouds.org/ros/branches/fuerte/perception_pcl
Package Summary
The Point Cloud Library (or PCL) for point cloud processing - development The PCL framework contains numerous state-of-the art algorithms including filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
- Maintainer: Julius Kammerl <jkammerl AT willowgarage DOT com>
- Author: Open Perception
- License: BSD
- External website: http://www.pointclouds.org
- Bug / feature tracker: http://dev.pointclouds.org
- Source: git https://github.com/ros-gbp/pcl-release.git (branch: release/pcl)
Package Summary
The Point Cloud Library (or PCL) for point cloud processing - development The PCL framework contains numerous state-of-the art algorithms including filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
- Maintainer: William Woodall <william AT osrfoundation DOT org>, Julius Kammerl <jkammerl AT willowgarage DOT com>
- Author: Open Perception
- License: BSD
- External website: http://www.pointclouds.org
- Bug / feature tracker: http://dev.pointclouds.org
- Source: git https://github.com/ros-gbp/pcl-release.git (branch: release/hydro/pcl)
Introduction
The Point Cloud Library (PCL) is a stand-alone C++ library for 3D point cloud processing. You can learn more about PCL by visiting its website, pointclouds.org. The documentation on ROS.org will help you get started using PCL in your ROS applications.
In particular, if you're just getting started with PCL in ROS, we encourage you to make use of the following resources:
Tutorials - Try out a few examples to get an idea of what PCL can do.
API documentation - Browse the online API reference to find information about PCL's various classes and functions.
Mailing list - If you need help, just ask! PCL has a great user community, with lots of people willing to help answer any questions you might have.
For new users of PCL, those resources will provide you with most of the information you'll need to get familiar with the library. Additional information pertaining to using PCL in your ROS applications can be found here on this wiki.
Using PCL in ROS
For information about how to use PCL's ROS-specific data types and how to publish and subscribe to point cloud data, please consult the PCL/ROS overview.
Tutorials
You can find numerous code examples on PCL's tutorials page.
For examples of how to include PCL code in a ROS node, please refer to the Tutorials page.
Documentation
For a detailed reference of PCL's classes and functions, please consult the online API documentation.
For a detailed reference of PCL's classes and functions, please consult the online API documentation.
For a reference guide to PCL's ROS-specific APIs, see the API documentation for the pcl_ros package.