A monte carlo algorithm for multi-robot localization

Dieter Fox, W. Burgard, H.Kruppa, and Sebastian Thrun
tech. report CMU-CS-99-120, Computer Science Department, Carnegie Mellon University, 1999


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Abstract
This paper presents a statistical 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.

Notes

Text Reference
Dieter Fox, W. Burgard, H.Kruppa, and Sebastian Thrun, "A monte carlo algorithm for multi-robot localization," tech. report CMU-CS-99-120, Computer Science Department, Carnegie Mellon University, 1999

BibTeX Reference
@techreport{Fox_1999_2669,
   author = "Dieter Fox and W. Burgard and H.Kruppa and Sebastian Thrun",
   title = "A monte carlo algorithm for multi-robot localization",
   booktitle = "",
   institution = "Computer Science Department",
   year = "1999",
   number= "CMU-CS-99-120",
   address= "Pittsburgh, PA",
}