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Journal of Intelligent and Robotic Systems

, Volume 51, Issue 4, pp 461–488 | Cite as

An Efficient Localization Algorithm Based on Vector Matching for Mobile Robots Using Laser Range Finders

  • Hee Jin Sohn
  • Byung Kook Kim
Unmanned System Paper

Abstract

This paper describes an efficient localization algorithm based on a vector-matching technique for mobile robots with laser range finders. As a reference the method uses a map with line-segment vectors, which can be built from a CAD layout of the indoor environment. The contribution of this work lies in the overall localization process. First, the proposed sequential segmentation method enables efficient vector extraction from scanned data. Second, a reliable and efficient vector-matching technique is proposed. Finally, a cost function suitable for vector-matching is proposed for nonlinear pose estimation with solutions for both nonsingular and singular cases. Simulation results show that the proposed localization method works reliably even in a noisy environment. The algorithm was implemented for our wheelchair-based mobile robot and evaluated in a 135 m long corridor environment.

Keywords

Laser range finder Localization Line segment extraction Line segment matching Map-based navigation 

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References

  1. 1.
    Anousaki, G.C., Kyriakopoulos, K.J.: Simultaneous localization and map building for mobile robot navigation. IEEE Robot. Autom. Mag. 6(3), 42–53 (1999)CrossRefGoogle Scholar
  2. 2.
    Arras, K.O., Siegwart, R.: Feature extraction and scene interpretation for map-based navigation and map building. In: Proceedings of the Symposium on Intelligent Systems and Advanced Manufacturing, vol. 3210, pp. 4253–4264. Mobile Robotics XII, Pittsburgh (1997)Google Scholar
  3. 3.
    Arras, K.O., Castellanos, J.A., Schilt, M., Siegwart, R.: Feature-based multi-hypothesis localization and tracking using geometric constraints. Robot. Auton. Syst. 44(1), 41–53 (2003)CrossRefGoogle Scholar
  4. 4.
    Ayache, N., Faugeras, O.: Hyper: a new approach for the recognition and positioning of 2D objects. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 44–54 (1986)Google Scholar
  5. 5.
    Beveridge, J.R., Riseman, E.M.: How easy is matching 2D line models using local search? IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 564–579 (1997)CrossRefGoogle Scholar
  6. 6.
    Borges, G.A., Aldon, M.J.: Optimal mobile robot pose estimation using geometrical maps. IEEE Trans. Robot. Autom. 18(1), 87–94 (2002)CrossRefGoogle Scholar
  7. 7.
    Borges, G.A., Aldon, M.J.: Line extraction in 2D range images for mobile robotics. J Intell. Robot. Syst. 40(1), 267–297 (2004)CrossRefGoogle Scholar
  8. 8.
    Borges, G.A., Aldon, M.J.: Robustified estimation algorithms for mobile robot localization based on geometrical environment maps. Robot. Auton. Syst. 45(3), 131–159 (2003)CrossRefGoogle Scholar
  9. 9.
    Castellanos, J., Tardós, J.: Laser-based segmentation and localization for a mobile robot. In: Robotics and Manufacturing: Recent Trends in Research and Applications, vol. 6, pp. 101–108. ASME Press, New York (1996)Google Scholar
  10. 10.
    Cox, I.J.: Blanche – an experiment in guidance and navigation of an autonomous robot vehicle. IEEE Trans. Robot. Autom. 7(2), 193–204 (1991)CrossRefGoogle Scholar
  11. 11.
    Dudek, G., Zhang, C.: Vision-based robot localization without explicit object models. In: Proceedings of International Conference on Robotics and Automation, vol. 1, pp. 76–81. IEEE, Minneapolis (1996)Google Scholar
  12. 12.
    Einsele, T.: Localization in indoor environments using a panoramic laser range finder. Dissertation, Technischen Universität München (2000)Google Scholar
  13. 13.
    Elfes, A.: Sonar-based real-world mapping and navigation. Journal of Robotics and Automation 3(3), 249–265 (1987)CrossRefGoogle Scholar
  14. 14.
    Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte Carlo localization: efficient position estimation for mobile robots. In: Proceedings of the National Conference on Artificial Intelligence, pp. 343–349. AAAI, Orlando (1999)Google Scholar
  15. 15.
    Haralick, R.: Propagating covariances in computer vision. In: Proceedings of the International Conference on Pattern Recognition, vol. 12, pp. 493–498. IEEE Computer Society Press, Los Alamitos (1994)CrossRefGoogle Scholar
  16. 16.
    Lingemann, K., Nüchter, A., Hertzberg, J., Surmann, H.: High-speed laser localization for mobile robots. Robot. Auton. Syst. 51(4), 275–296 (2005)CrossRefGoogle Scholar
  17. 17.
    Sohn, H.J., Kim, B.K.: A robust localization algorithm for mobile robots with laser range finders. In: Proceedings of International Conference on Robotics and Automation, pp. 3545–3550. IEEE, Barcelona (2005)Google Scholar
  18. 18.
    Jensfelt, P., Kristensen, S.: Active global localization for a mobile robot using multiple hypothesis tracking. IEEE Trans. Robot. Autom. 17(5), 748–760 (2001)CrossRefGoogle Scholar
  19. 19.
    Lu, F., Milios, E.: Robot pose estimation in unknown environments by matching 2D range scans. J. Intell. Robot. Syst. 18(3), 249–275 (1997)CrossRefGoogle Scholar
  20. 20.
    Minguez, J., Lamiraux, F., Montesano, L.: Metric-based scan matching algorithms for mobile robot displacement estimation. In: Proceedings of International Conference on Robotics and Automation, pp. 3557–3563. IEEE, Barcelona (2005)Google Scholar
  21. 21.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of the National Conference on Artificial Intelligence, vol. 18, pp. 593–598. AAAI, Orlando (2003)Google Scholar
  22. 22.
    Mota, J.G., Ribeiro, M.I.: Mobile robot localisation on reconstructed 3D models. J. Robot. Auton. Syst. 31(1), 11–30 (2000)Google Scholar
  23. 23.
    Nguyen, V., Martinelli, A., Tomatis, N., Siegwart, R.: A comparison of line extraction algorithms using 2D laser rangefinder for indoor mobile robotics. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, vol. 2005, pp. 1929–1934. IEEE, Alberta (2005)Google Scholar
  24. 24.
    Pavlidis, T., Horowitz, S.: Segmentation of plane curves. IEEE Trans. Comput. 23, 860–870 (1974)MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Pfister, S., Roumeliotis, S., Burdick, J.: Weighted line fitting algorithms for mobile robot map building and efficient data representation. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1304–1311. IEEE, Taipei (2003)Google Scholar
  26. 26.
    Pfister, S.: Algorithms for mobile robot localization and mapping, incorporating detailed noise modeling and multi-scale feature extraction. Dissertation, California Institute of Technology (2006)Google Scholar
  27. 27.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of the 3rd International Conference on 3D Digital Imaging and Modeling, pp. 145–152. IEEE, Quebec (2001)CrossRefGoogle Scholar
  28. 28.
    Siegwart, R., Nourbakhsh, I.: Introduction to Autonomous Mobile Robots. MIT Press, Cambridge (2004)Google Scholar
  29. 29.
    Weber, J., Jörg, K.W., Puttkamer, E.: APR-Global scan matching using anchor point relationships. In: Proceedings of the 6th International Conference on Intelligent Autonomous Systems, pp. 471–478. IOS press, Venice (2000)Google Scholar
  30. 30.
    Weiß, G., Wetzler, C., Puttkamer, E.: Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans. In: Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, vol. 1, pp. 595–601. IEEE, Munich (1994)Google Scholar
  31. 31.
    Zezhong, X., Ronghua, L., Jilin, L.: Global localization based on corner point. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol. 2, pp. 843–847. IEEE, Kobe (2003)CrossRefGoogle Scholar
  32. 32.
    Zhang, L., Ghosh, B.K.: Line segment based map building and localization using 2D laser rangefinder. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 2538–2543. IEEE, San Francisco (2000)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceKorea Advanced Institute of Science and TechnologyDaejeonSouth Korea

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