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Landmark Based Global Self-localization of Mobile Soccer Robots

  • Abdul Bais
  • Robert Sablatnig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3852)

Abstract

We present a stereo vision based global self-localization strategy for tiny autonomous mobile robots in a well-known dynamic environment. Global localization is required for an initial startup or when the robot loses track of its pose during navigation. Existing approaches are based on dense range scans, active beacon systems, artificial landmarks, bearing measurements using omni-directional cameras or bearing/range calculation using single frontal cameras, while we propose feature based stereo vision system for range calculation. Location of the robot is estimated using range measurements with respect to distinct landmarks such as color transitions, corners, junctions and line intersections. Unlike methods based on angle measurement, this method requires only two distinct landmarks. Simulation results show that robots can successfully localize themselves whenever two distinct landmarks are observed. As such marked minimization of landmarks for vision based self-localization of robots has been achieved.

Keywords

Mobile Robot Global Coordinate System Color Patch Landmark Point Robot Position 
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.

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References

  1. 1.
    Borenstein, J., Everett, H.R., Feng, L.: Navigating Mobile Robots: Systems and Techniques. A. K. Peters, Ltd, Stanford (1996)zbMATHGoogle Scholar
  2. 2.
    Iocchi, L., Nardi, D.: Hough Localization for mobile robots in polygonal environments. Robotics and Autonomous Systems 40, 43–58 (2002)CrossRefGoogle Scholar
  3. 3.
    Enderle, S., Ritter, M., Fox, D., Sablatnög, S., Kraetzschmar, G., Palm, G.: Soccer robot localization using sporadic visual features. In: E.P., et al. (ed.) International Conference on Intelligent Autonomous Systems 6 (IAS-6), pp. 959–966 (2000)Google Scholar
  4. 4.
    Adorni, G., Cagnoni, S., Enderle, S., Kraetzschmar, G.K.: Vision-based localization for mobile robots. Robotics and Autonomous Systems 36, 103–119 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Motomura, A., Matsuoka, T., Hasegawa, T.: Self-localization method using two landmarks and dead reckoning for autonomous mobile soccer robots. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS, vol. 3020, pp. 526–533. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Adorni, G., Cagnoni, S., Mordonini, M.: Landmark-based robot self-localization: a case study for the robocup goal-keeper. In: Proceedings of the International Conference on Information Intelligence and Systems, pp. 164–171 (1999)Google Scholar
  7. 7.
    de Jong, F., Caarls, J., Bartelds, R., Jonker, P.: A two-tiered approach to self-localization. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS, vol. 2377, pp. 405–410. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Tehrani, A.F., Rojas, R., Moballegh, H.R., Hosseini, I., Amini, P.: Analysis by synthesis, a novel method in mobile robot self-localization. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS, vol. 3276, pp. 586–593. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Utz, H., Neubeck, A., Mayer, G., Kraetzschmar, G.: Improving vision-based self-localization. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS, vol. 2752, pp. 25–40. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Christensen, H.I., Kirkeby, N.O., Kristensen, S., Knudsen, L.: Model-driven vision for in-door navigation. Robotics and Autonomous Systems 12, 199–207 (1994)CrossRefGoogle Scholar
  11. 11.
    Gutmann, J., Schlegel, C.: Amos: Comparison of scan matching approaches for self-localization in indoor environments. In: 1st Euro micro Workshop on Advanced Mobile Robots. IEEE Computer Society Press, Los Alamitos (1996)Google Scholar
  12. 12.
    Grisetti, G., Iocchi, L., Nardi, D.: Global Hough localization for mobile robots in polygonal environments. In: IEEE International Conference on Robotics and Automation (ICRA 2002), pp. 353–358 (2002)Google Scholar
  13. 13.
    Marques, C.F., Lima, P.U.: A localization method for a soccer robot using a vision-based omni-directional sensor. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS, vol. 2109, pp. 96–107. Springer, Heidelberg (2001)Google Scholar
  14. 14.
    Duda, R., Hart, P.: Use of the Hough transformation to detect lines and curves in the pictures. Communications of the ACM 15, 11–15 (1972)CrossRefGoogle Scholar
  15. 15.
    Stroupe, A.W., Sikorski, K., Balch, T.: Constraint-based landmark localization. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS, vol. 2752, pp. 8–24. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Bandlow, T., Klupsch, M., Hanek, R., Schmitt, T.: Fast image segmentation, object recognition, and localization in a roboCup scenario. In: Veloso, M.M., Pagello, E., Kitano, H. (eds.) RoboCup 1999. LNCS (LNAI), vol. 1856, pp. 174–185. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  17. 17.
    Choi, W., Ryu, C., Kim, H.: Navigation of a mobile robot using mono-vision and mono-audition. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 1999), vol. 4, pp. 686–691 (1999)Google Scholar
  18. 18.
    Herrero-Pérez, D., Martínez-Barberá, H., Saffiotti, A.: Fuzzy self-localization using natural features in the four-legged league. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS, vol. 3276, pp. 110–121. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Nickerson, S.B., Jasiobedzki, P., Wilkes, D., Jenkin, M., Milios, E., Tsotsos, J., Jepson, A., Bains, O.N.: The ark project: Autonomous mobile robots for known industrial environments. Robotics and Autonomous Systems 25, 83–104 (1998)CrossRefGoogle Scholar
  20. 20.
    Bais, A., Sablatnig, R., Novak, G.: Line-based landmark recognition for self-localization of soccer robots. In: IEEE International Conference on Emerging Technologies (ICET 2005), Islamabad, Pakistan, pp. 132–137 (2005)Google Scholar
  21. 21.
    Novak, G., Mahlknecht, S.: TINYPHOON a tiny autonomous mobile robot. In: IEEE International Symposium on Industrial Electronics (ISIE 2005), pp. 1533–1538 (2005)Google Scholar
  22. 22.
    Sugihara, K.: Some location problems for robot navigation using a single camera. Computer Vision, Graphics, and Image Processing 42, 112–129 (1988)CrossRefGoogle Scholar
  23. 23.
    Sutherland, K.T., Thompson, B.W.: Inexact navigation. In: IEEE International Conference on Robotics and Automation (ICRA 1993), pp. 1–7 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Abdul Bais
    • 1
  • Robert Sablatnig
    • 2
  1. 1.Institute of Computer TechnologyVienna University of TechnologyViennaAustria
  2. 2.Pattern Recognition and Image Processing Group, Institute of Computer Aided AutomationVienna University of TechnologyViennaAustria

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