Skip to main content
SpringerLink
Log in

Localization for Mobile Robot Teams: A Distributed MLE Approach

  • Conference paper
  • First Online:
Experimental Robotics VIII

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 5))

Abstract

This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks. We assume that robots are equipped with sensors that allow them to measure the relative pose and identity of nearby robots, as well as sensors that allow them to measure changes in their own pose. Using a combination of maximum likelihood estimation and distributed numerical optimization, we can, for each robot, estimate the relative range, bearing, and orientation of every other robot in the team. This paper describes the basic formalism, its distributed implementation, and presents experimental results obtained using a team of four mobile robots.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. W. M. G. Dissanayake, P. Newman, S. Clark, H. F. Durrant-Whyte, and M. Csorba. A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation, 17(3):229–241, 2001.

    Article  Google Scholar 

  2. T. Duckett, S. Marsland, and J. Shapiro. Learning globally consistent maps by relaxation. In Proceedings of the IEEE International Conference on Robotics and Automation, volume 4, pages 3841–3846, San Francisco, U.S.A., 2000.

    Google Scholar 

  3. D. Fox, W. Burgard, H. Kruppa, and S. Thrun. A probabilistic approach to collaborative multi-robot localization. Autonomous Robots, Special Issue on Heterogeneous Multi-Robot Systems, 8(3):325–344, 2000.

    Google Scholar 

  4. D. Fox, W. Burgard, and S. Thrun. Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11:391–427, 1999.

    MATH  Google Scholar 

  5. M. Golfarelli, D. Maio, and S. Rizzi. Elastic correction of dead reckoning errors in map building. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, volume 2, pages 905–911, Victoria, Canada, 1998.

    Google Scholar 

  6. A. Howard and L. Kitchen. Cooperative localisation and mapping. In International Conference on Field and Service Robotics (FSR99), pages 92–97, 1999.

    Google Scholar 

  7. A. Howard, M. J. Matarić, and G. S. Sukhatme. Localization for mobile robot teams: A maximum likelihood approach. Technical Report IRIS-01-407, Institute for Robotics and Intelligent Systems Technical Report, University of Sourthern California, 2001.

    Google Scholar 

  8. A. Howard, M. J. Matarić, and G. S. Sukhatme. Team localization: A maximum likelihood approach. Technical Report IRIS-01-415, Institute for Robotics and Intelligent Systems Technical Report, University of Sourthern California, 2002.

    Google Scholar 

  9. R. Kurazume and S. Hirose. An experimental study of a cooperative positioning system. Autonomous Robots, 8(1):43–52, 2000.

    Article  Google Scholar 

  10. J. J. Leonard and H. F. Durrant-Whyte. Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation, 7(3):376–382, 1991.

    Article  Google Scholar 

  11. F. Lu and E. Milios. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4:333–349, 1997.

    Article  Google Scholar 

  12. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 2nd edition, 1999.

    Google Scholar 

  13. I. M. Rekleitis, G. Dudek, and E. Milios. Multi-robot exploration of an unknown environment: efficiently reducing the odometry error. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), volume 2, pages 1340–1345, 1997.

    Google Scholar 

  14. S. I. Roumeliotis and G. A. Bekey. Collective localization: a distributed Kalman filter approach. In Proceedings of the IEEE International Conference on Robotics and Automation, volume 3, pages 2958–2965, San Francisco, U.S.A., 2000.

    Google Scholar 

  15. R. Simmons and S. Koenig. Probabilistic navigation in partially observable environments. In Proceedings of International Joint Conference on Artificial Intelligence, volume 2, pages 1080–1087, 1995.

    Google Scholar 

  16. S. Thrun. A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5):335–363, 2001.

    Article  Google Scholar 

  17. S. Thrun, D. Fox, and W. Burgard. A probabilistic approach to concurrent mapping and localisation for mobile robots. Machine Learning, 31(5):29–55, 1998. Joint issue with Autonomous Robots.

    Article  MATH  Google Scholar 

  18. B. Yamauchi, A. Shultz, and W. Adams. Mobile robot exploration and map-building with continuous localization. In Proceedings of the 1998 IEEE/RSJ International Conference on Robotics and Automation, volume 4, pages 3175–3720, San Francisco, U.S.A., 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics Research Laboratories, Department of Computer Science, University of Southern California, Los Angeles, CA, 90089-0781, USA

    Andrew Howard, Maja J. Matarić & Gaurav S. Sukhatme

Authors
  1. Andrew Howard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maja J. Matarić
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gaurav S. Sukhatme
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione e Ingegneria Elettrica, Università degli Studi di Salerno, Via Ponte Don Melillo, 84084, Fisciano (SA), Italy

    Bruno Siciliano

  2. Scuola Superiore di Studi Universitari e di Perfezionamento Sant’Anna, Piazza Martiri della Libertà 33, 56127, Pisa, Italy

    Paolo Dario

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Howard, A., Matarić, M.J., Sukhatme, G.S. (2003). Localization for Mobile Robot Teams: A Distributed MLE Approach. In: Siciliano, B., Dario, P. (eds) Experimental Robotics VIII. Springer Tracts in Advanced Robotics, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36268-1_12

Download citation

  • DOI: https://doi.org/10.1007/3-540-36268-1_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00305-2

  • Online ISBN: 978-3-540-36268-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation