D. Fox, W. Burgard, H.Kruppa, and S. Thrun

Efficient Multi-Robot Localization Based on Monte Carlo Approximation

Proc. of the 9th International Symposium of Robotics Research (ISRR-99), 1999


This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range-finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.


Full paper [.ps.gz] (1268 kb, 8 pages)

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