Advertisement

Self-localization in Large-Scale Environments for the Bremen Autonomous Wheelchair

  • Axel Lankenau
  • Thomas Röfer
  • Bernd Krieg-Brückner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2685)

Abstract

This paper presents RouteLoc, a new approach for the absolute self-localization of mobile robots in structured large-scale environments. As experimental platform, the Bremen Autonmous Wheelchair “Rolland” is used on a 2, 176m long journey across the campus of the Universität Bremen. RouteLoc poses only very low requirements with regard to sensor input, resources (memory, computing time), and a-priori knowledge. The approach is based on a hybrid topological-metric representation of the environment. It scales up very well, and is thus suitable for self-localization of service robots in large-scale environments. The evaluation of RouteLoc is done with a pure metric approach as reference method.It compares scan-matching results of laser range finder data with the position estimates of RouteLoc on a metric basis.

Keywords

Mobile Robot Decision Point Service Robot Generalization Algorithm Matching Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Borenstein, H.R. Everett, and L. Feng. Navigating Mobile Robots — Systems and Techniques. A.K. Peters, Ltd., USA, 1996.zbMATHGoogle Scholar
  2. 2.
    W. Burgard, D. Fox, and D. Henning. Fast grid-based position tracking for mobile robots. In G. Brewka, Ch. Habel, and B. Nebel, editors, KI-97: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, pages 289–300, Berlin, Heidelberg, New York, 1997. Springer.Google Scholar
  3. 3.
    H. Choset and K. Nagatani. Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation, 17(2):125–136, April 2001.CrossRefGoogle Scholar
  4. 4.
    A. Elfes. Occupancy grids: A stochastic spatial representation for active robot perception. In S. S. Iyengar and A. Elfes, editors, Autonomous Mobile Robots, volume 1, pages 60–70, Los Alamitos, California, 1991. IEEE Computer Society Press.Google Scholar
  5. 5.
    S.P. Engelson and D.V. McDermott. Error correction in mobile robot map learning. In Proceedings of the IEEE Int.’l Conf. on Robotics and Automation, pages 2555–2560, Nice, France, May 1992. IEEE.Google Scholar
  6. 6.
    C. Eschenbach, C. Habel, L. Kulik, and A. Leßmöllmann. Shape Nouns and Shape Concepts: A Geometry for ‘Corner’, volume 1404 of Lecture Notes in Artificial Intelligence, pages 177–201. Springer, Berlin, Heidelberg, New York, 1998.Google Scholar
  7. 7.
    D. Fox, W. Burgard, F. Dellaert, and S. Thrun. Monte Carlo localization: Efficient position estimation for mobile robots. In Proc. of the National Conference on Artificial Intelligence, 1999.Google Scholar
  8. 8.
    J.-S. Gutmann and B. Nebel. Navigation mobiler Roboter mit Laserscans. In P. Levi, Th. Braunl, and N. Oswald, editors, Autonome Mobile Systeme, Informatik aktuell, pages 36–47, Berlin, Heidelberg New York, 1997. Springer.Google Scholar
  9. 9.
    J.-S. Gutmann, T. Weigel, and B. Nebel. A fast, accurate, and robust method for self-localization in polygonial environments using laser-range-finders. Advanced Robotics, 14(8):651–668, 2001.CrossRefGoogle Scholar
  10. 10.
    J. Kollmann and T. Röfer. Echtzeitkartenaufbau mit einem 180°-Laser-Entfernungssensor. In R. Dillmann, H. Wörn, and M. von Ehr, editors, Autonome Mobile Systeme 2000, Informatik aktuell, pages 121–128. Springer, 2000.Google Scholar
  11. 11.
    B. Kuipers, R. Froom, Y.W. Lee, and D. Pierce. The semantic hierarchy in robot learning. In J. Connell and S. Mahadevan, editors, Robot Learning, pages 141–170. Kluwer Academic Publishers, 1993.Google Scholar
  12. 12.
    A. Lankenau and T. Röfer. The Bremen Autonomous Wheelchair — a versatile and safe mobility assistant. IEEE Robotics and Automation Magazine, “Reinventing the Wheelchair”, 7(1): 29–37, March 2001.CrossRefGoogle Scholar
  13. 13.
    F. Lu and E. Milios. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4:333–349, 1997.CrossRefGoogle Scholar
  14. 14.
    A. Mojaev and A. Zell. Online-Positionskorrektur für mobile Roboter durch Korrelation lokaler Gitterkarten. In H. Wörn, R. Dillmann, and D. Henrich, editors, Autonome Mobile Systeme, Informatik aktuell, pages 93–99, Berlin, Heidelberg, New York, 1998. Springer.Google Scholar
  15. 15.
    A. Musto, K. Stein, A. Eisenkolb, and T. Röfer. Qualitativ e and quantitative representations of locomotion and their application in robot navigation. In Proc. of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99), pages 1067–1073, San Francisco, CA, 1999. Morgan Kaufman Publishers, Inc.Google Scholar
  16. 16.
    I. Nourbakhsh, R. Powers, and S. Birchfield. Dervish: An office-navigating robot. AI Magazine, 16: 53–60, 1995.Google Scholar
  17. 17.
    T. Röfer. Strategies for using a simulation in the development of the Bremen Autonomous Wheelchair. In R. Zobel and D. Moeller, editors, Simulation-Past, Present and Future, pages 460–464. Society for Computer Simulation International, 1998.Google Scholar
  18. 18.
    T. Röfer. Route navigation using motion analysis. In Proc. Conf. on Spatial Information Theory’ 99, volume 1661 of Lecture Notes in Artificial Intelligence, pages 21–36, Berlin, Heidelberg, New York, 1999. Springer.Google Scholar
  19. 19.
    T. Röfer. Building consistent laser scan maps. In Proc. of the 4th European Workshop on Advanced Mobile Robots (Eurobot 2001), volume 86 of Lund University Cognitive Studies, pages 83–90, 2001.Google Scholar
  20. 20.
    T. Röfer. Konsisten te Karten aus Laser Scans. In Autonome Mobile Systeme 2001, Informatik aktuell, pages 171–177. Springer, 2001.Google Scholar
  21. 21.
    T. Röfer and A. Lankenau. Ensuring safe obstacle avoidance in a shared-control system. In J.M. Fuertes, editor, Proc. of the 7th Int. Conf. on Emergent Technologies and Factory Automation, pages 1405–1414, 1999.Google Scholar
  22. 22.
    T. Röfer and A. Lankenau. Architecture and applications of the Bremen Autonomous Wheelchair. Information Sciences, 126(12-4):1–20, July 2000.zbMATHCrossRefGoogle Scholar
  23. 23.
    J.S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, New Jersey, USA, 1995.zbMATHGoogle Scholar
  24. 24.
    R. Simmons and S. Koenig. Probabilistic robot navigation in partially observable environments. In Proc. of the Int. Joint Conf. on Artificial Intelligence, IJCAI-95, pages 1080–1087, 1995.Google Scholar
  25. 25.
    S. Thrun. Learning maps for indoor mobile robot navigation. Artificial Intelligence, 99:21–71, 1998.zbMATHCrossRefGoogle Scholar
  26. 26.
    S. Thrun, W. Burgard, and D. Fox. A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping. In Proc. of the IEEE Int. Conf. on Robotics & Automation, pages 321–328, 2000.Google Scholar
  27. 27.
    S. Thrun, D. Fox, W. Burgard, and F. Dellaert. Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 101:99–141, 2000.Google Scholar
  28. 28.
    N. Tomatis, I. Nourbakhsh, and R. Siegwart. Simultaneous localization and map building: A global topological model with local metric maps. In Proceedings of the IEEE/RSJ Int.’l Conf. on Intelligent Robots and Systems (IROS 2001), Maui, Hawaii, October 2001.Google Scholar
  29. 29.
    G. Weiß, C. Wetzler, and E. von Puttkamer. Keeping Track of Position and Orientation of Moving Indoor Systems by Correlation of Range-Finder Scans. In Proc. Int. Conf. on Intelligent Robots and Systems 1994 (IROS-94), pages 595–601, 1994.Google Scholar
  30. 30.
    S. Werner, B. Krieg-Br:uckner, and Th. Herrmann. Modelling Navigational Knowledge by Route Graphs, volume 1849 of Lecture Notes in Artificial Intelligence, pages 295–316. Springer, Berlin, Heidelberg, New York, 2000.Google Scholar
  31. 31.
    D. van Zwynsvoorde, T. Simeon, and R. Alami. Incremental topological modeling using local Voronoï-like graphs. In Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and System (IROS 2000), volume 2, pages 897–902, Takamatsu, Japan, October 2000.CrossRefGoogle Scholar
  32. 32.
    D. van Zwynsvoorde, T. Simeon, and R. Alami. Building topological models for navigation in large scale environments. In Proc. of IEEE Int. Conf. on Robotics and Automation ICRA 2001, pages 4256–4261, Seoul, Korea, May 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Axel Lankenau
    • 1
  • Thomas Röfer
    • 1
  • Bernd Krieg-Brückner
    • 1
  1. 1.Bremer Institut für Sichere Systeme, TZI, FB3Universität BremenBremenGermany

Personalised recommendations