Long Description
The central challenge in robotic mapping is obtaining reliable data associations (or loop closures): state-of-the-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will still encounter errors, leading to system failure. We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, we optimize the map while simultaneously determining which loop closures are correct from within a single integrated Bayesian framework. Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for real-time performance. Our method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the front-end loop-validation systems can be unnecessary. The package contains the code and a plugin for g2o with some sample datasets.

Hardware/Software Requirements
All requirements of g2o.

Papers Describing the Approach
Edwin Olson and Pratik Agarwal: Inference on networks of mixtures for robust robot mapping, Proceedings of Robotics: Science and Systems, 2012 (link)

Edwin Olson and Pratik Agarwal: Inference on networks of mixtures for robust robot mapping, Internation Journal of Robotics Research (To Appear), 2013

License Information
This software 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.
The authors allow the users of OpenSLAM.org to use and modify the source code for their own research. Any commercial application, redistribution, etc has to be arranged between users and authors individually and is not covered by OpenSLAM.org.