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Collaborative Multi-robot Localization

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Book cover KI-99: Advances in Artificial Intelligence (KI 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1701))

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Abstract

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. The robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

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Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213

    Dieter Fox & Sebastian Thrun

  2. Computer Science Department III, University of Bonn, D-53117, Bonn, Germany

    Wolfram Burgard

  3. Department of Computer Science, ETH Zurich, CH-8092, Zurich, Switzerland

    Hannes Kruppa

Authors
  1. Dieter Fox
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  2. Wolfram Burgard
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  3. Hannes Kruppa
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  4. Sebastian Thrun
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Editor information

Editors and Affiliations

  1. Institut für Informatik III, Universität Bonn, Römerstraße 164, D-53117, Bonn, Germany

    Wolfram Burgard  & Armin B. Cremers  & 

  2. GMD-Forschungszentrum Informationstechnik GmbH, Schloß Birlinghoven, D-53754, Sankt Augustin, Germany

    Thomas Cristaller

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© 1999 Springer-Verlag Berlin Heidelberg

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Fox, D., Burgard, W., Kruppa, H., Thrun, S. (1999). Collaborative Multi-robot Localization. In: Burgard, W., Cremers, A.B., Cristaller, T. (eds) KI-99: Advances in Artificial Intelligence. KI 1999. Lecture Notes in Computer Science(), vol 1701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48238-5_21

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  • DOI: https://doi.org/10.1007/3-540-48238-5_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66495-6

  • Online ISBN: 978-3-540-48238-3

  • eBook Packages: Springer Book Archive

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