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Algorithmic Foundations of Robotics V pp 363–380Cite as

Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results

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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 7))

Abstract

This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot. Many of today’s popular techniques are based on extended Kalman filters (EKFs), which require update time quadratic in the number of features in the map. This paper develops the notion of sparse extended information filters (SEIFs), as a new method for solving the SLAM problem. SEIFs exploit structure inherent in the SLAM problem, representing maps through local, Web-like networks of features. By doing so, updates can be performed in constant time, irrespective of the number of features in the map. This paper presents several original constant-time results of SEIFs, and provides simulation results that show the high accuracy of the resulting maps in comparison to the computationally more cumbersome EKF solution.

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References

  1. M. Bosse, J. Leonard, and S. Teller. Large-scale CML using a network of multiple local maps. In [11].

    Google Scholar 

  2. W. Burgard, D. Fox, H. Jans, C. Matenar, and S. Thrun. Sonar-based mapping of large-scale mobile robot environments using EM. Proc. OCML-99.

    Google Scholar 

  3. H. Choset. Sensor Based Motion Planning: The Hierarchical Generalized Voronoi Graph. PhD thesis, Caltech, 1996.

    Google Scholar 

  4. M. Csorba. Simultaneous Localization and Map Building. PhD thesis, Univ. of Oxford, 1997.

    Google Scholar 

  5. M. Deans and M. Hebert. Invariant filtering for simultaneous localization and mapping. Proc. ICRA-OO.

    Google Scholar 

  6. G. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte, and M. Csorba. A solution to the simultaneous localisation and map building (SLAM) problem. Transactions of Robotics and Automation, 2001.

    Google Scholar 

  7. A. Doucet, J.F.G. de Freitas, and N.J. Gordon, editors. Sequential Monte Carlo Methods In Practice. Springer, 2001.

    Chapter  Google Scholar 

  8. J. Guivant and E. Nebot. Optimization of the simultaneous localization and map building algorithm for real time implementation. Transactions of Robotics and Automation, 2001.

    Google Scholar 

  9. J.-S. Gutmann and K. Konolige. Incremental mapping of large cyclic environments. Proc. ICRA-00.

    Google Scholar 

  10. B. Kuipers and Y.-T. Byun. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems, 8, 1991.

    Google Scholar 

  11. J. Leonard, J.D. Tardös, S. Thrun, and H. Choset, editors. ICRA Workshop Notes (W4), 2002.

    Google Scholar 

  12. J. J. Leonard and H. F. Durrant-Whyte. Directed Sonar Sensing for Mobile Robot Navigation. Kluwer, 1992.

    Chapter  Google Scholar 

  13. J.J. Leonard and H.J.S. Feder. A computationally efficient method for large-scale concurrent mapping and localization.

    Google Scholar 

  14. Y. Liu and S. Thrun. Results for outdoor-SLAM using sparse extended information filters. Submitted to ICRA-03.

    Google Scholar 

  15. M. J. Mataric. A distributed model for mobile robot environment-learning and navigation. MIT AITR-1228.

    Google Scholar 

  16. P. Maybeck. Stochastic Models, Estimation, and Control, Volume 1. Academic Press, 1979.

    Google Scholar 

  17. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit. FastSLAM: A factored solution to the simultaneous localization and mapping problem. Proc. AAAI-02.

    Google Scholar 

  18. P. Moutarlier and R. Chatila. An experimental system for incremental environment modeling by an autonomous mobile robot. Proc. ISER-89.

    Google Scholar 

  19. K. Murphy. Bayesian map learning in dynamic environments. Proc. NIPS-99.

    Google Scholar 

  20. E. Nettleton, H. Durrant-Whyte, P. Gibbens, and A. Goktogan. Multiple platform localisation and map building. Sensor Fusion and Decentralised Control in Robotic Sty stems III: 4196, 2000.

    Google Scholar 

  21. E. Nettleton, S. Thrun, and H. Durrant-Whyte. A constant time communications algorithm for decentralised SLAM. Submitted.

    Google Scholar 

  22. E.W. Nettleton, P.W. Gibbens, and H.F. Durrant-Whyte. Closed form solutions to the multiple platform simultaneous localisation and map building (slam) problem. Sensor Fusion: Architectures, Algorithms, and Applications IV: 4051, 2000.

    Google Scholar 

  23. P. Newman. On the Structure and Solution of the Simultaneous Localisation and Map Building Problem. PhD thesis, University of Sydney, 2000.

    Google Scholar 

  24. H Shatkay and L. Kaelbling. Learning topological maps with weak local odometric information. Proc. IJCAI-97.

    Google Scholar 

  25. R. Simmons, D. Apfelbaum, W. Burgard, M. Fox, D. an Moors, S. Thrun, and H. Younes. Coordination for multi-robot exploration and mapping. Proc. AAAI-00.

    Google Scholar 

  26. R. Smith, M. Self, and P. Cheeseman. Estimating uncertain spatial relationships in robotics. Autonomous Robot Vehicles, Springer, 1990.

    Google Scholar 

  27. R. C. Smith and P. Cheeseman. On the representation and estimation of spatial uncertainty. TR 4760 & 7239, SRI, 1985.

    Google Scholar 

  28. C. Thorpe and H. Durrant-Whyte. Field robots. Proc. ISRR-01.

    Google Scholar 

  29. S. Thrun, D. Koller, Z. Ghahramani, H. Durrant-Whyte, and A.Y. Ng. Simultaneous mapping and localization with sparse extended information filters:theory and initial results. TR CMU-CS-02–112, CMU, 2002.

    Google Scholar 

  30. S. Thrun. Robotic mapping: A survey. Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann, 2002.

    Google Scholar 

  31. S. Thrun, D. Fox, and W. Burgard. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning, 31, 1998.

    Google Scholar 

  32. S. Williams and G. Dissanayake. Efficient simultaneous localisation and mapping using local submaps. In [11].

    Google Scholar 

  33. U.R. Zimmer. Robust world-modeling and navigation in a real world. Neurocomputing, 13, 1996.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Sebastian Thrun

  2. Stanford University, Stanford, CA, USA

    Daphne Koller & Andrew Y. Ng

  3. Gatsby Computational Neuroscience Unit, University College, London, UK

    Zoubin Ghahramani

  4. University of Sydney, Sydney, Australia

    Hugh Durrant-Whyte

Authors
  1. Sebastian Thrun
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  2. Daphne Koller
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  3. Zoubin Ghahramani
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  4. Hugh Durrant-Whyte
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  5. Andrew Y. Ng
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Editor information

Editors and Affiliations

  1. INRIA, 2004 Route des Lucioles, 06902, Sophia-Antipolis, France

    Jean-Daniel Boissonnat

  2. Dept. of Mechanical Engineering, California Inst. of Technology, East California Boulevard 12000, 91125, Pasadena, CA, USA

    Joel Burdick

  3. Dept. of Mechanical Engineering, University of California at Berkeley, 4189 Etcheverry Hall, 94720, Berkeley, CA, USA

    Ken Goldberg

  4. Beckman Inst., University of Illinois, North Matthews Avenue 405, 61801, Urbana, IL, USA

    Seth Hutchinson

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© 2004 Springer-Verlag Berlin Heidelberg

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Thrun, S., Koller, D., Ghahramani, Z., Durrant-Whyte, H., Ng, A.Y. (2004). Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results. In: Boissonnat, JD., Burdick, J., Goldberg, K., Hutchinson, S. (eds) Algorithmic Foundations of Robotics V. Springer Tracts in Advanced Robotics, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45058-0_22

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  • DOI: https://doi.org/10.1007/978-3-540-45058-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07341-0

  • Online ISBN: 978-3-540-45058-0

  • eBook Packages: Springer Book Archive

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