Multi-robot SLAM with sparse extended information filers.

S. Thrun and Y. Liu.

We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous--which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot poses by Gaussian Markov random fields. The alignment of local maps into a single global maps is achieved by a tree-based algorithm for searching similar-looking local landmark configurations, paired with a hill climbing algorithm that maximizes the overall likelihood by search in the space of correspondences. We report favorable results obtained with a real-world benchmark data set.

The full paper is available in PDF and gzipped Postscript

@INPROCEEDINGS{Thrun03e,
  AUTHOR	= {S. Thrun and Y. Liu},
  TITLE		= {Multi-Robot {SLAM} With Sparse Extended Information Filers},
  YEAR		= {2003},
  BOOKTITLE = 	 {Proceedings of the 11th International Symposium of Robotics Research (ISRR'03)},
  publisher     = {Springer},
  address       = {Sienna, Italy}  
}