MCPTAM is a set of ROS nodes for running Real-time 3D Visual Simultaneous Localization and Mapping (SLAM) using Multi-Camera Clusters. It includes tools for calibrating both the intrinsic and extrinsic parameters of the individual cameras within the rigid camera rig.
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README

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 *  
 *  
 *  Copyright 2014  Adam Harmat (McGill University) 
 *                      [adam.harmat@mail.mcgill.ca]
 *                  Michael Tribou (University of Waterloo)
 *                      [mjtribou@uwaterloo.ca]
 *
 *  Multi-Camera Parallel Tracking and Mapping (MCPTAM) is free software:
 *  you can redistribute it and/or modify it under the terms of the GNU 
 *  General Public License as published by the Free Software Foundation,
 *  either version 3 of the License, or (at your option) any later
 *  version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program.  If not, see <http://www.gnu.org/licenses/>.
 *  
 *  MCPTAM is based on the Parallel Tracking and Mapping (PTAM) software.
 *  Copyright 2008 Isis Innovation Limited
 *  
 *  
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MCPTAM is a set of ROS nodes for running Real-time 3D Visual Simultaneous Localization and Mapping (SLAM) using Multi-Camera Clusters. It includes tools for calibrating both the intrinsic and extrinsic parameters of the individual cameras within the rigid camera rig.

Visit the MCPTAM website (https://github.com/aharmat/mcptam).

For more information, refer to the MCPTAM Wiki (https://github.com/aharmat/mcptam/wiki).

A Getting-Started Guide is available on the Wiki, or a snapshot can be found in the file Getting-Started.pdf.

If you use this software, please cite the following papers:

A. Harmat, M. Trentini and I. Sharf "Multi-Camera Tracking and Mapping for Unmanned Aerial Vehicles in Unstructured Environments" in Journal of Intelligent and Robotic Systems, vol. 78, no. 2, pp. 291-317, May 2015 (http://link.springer.com/article/10.1007%2Fs10846-014-0085-y)
 
A. Harmat, I. Sharf and M. Trentini "Parallel Tracking and Mapping with Multiple Cameras on an Unmanned Aerial Vehicle" in Intelligent Robotics and Applications Lecture Notes in Computer Science, vol. 7506, pp. 421-432, 2012 (http://link.springer.com/chapter/10.1007%2F978-3-642-33509-9_42)