Gaussian Multi-Robot SLAM

Yufeng Liu and Sebastian Thrun

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.

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@UNPUBLISHED{Liu03a,
  AUTHOR	= {Y. Liu and S. Thrun},
  TITLE		= {Gaussian Multi-Robot {SLAM}},
  YEAR		= {2003},
  ORGANIZATION	= {Carnegie Mellon University},
  ADDRESS	= {Pittsburgh, PA},
  NOTE          = {Submitted for publication}
}