Active Single Landmark Based Global Localization of Autonomous Mobile Robots

  • Abdul Bais
  • Robert Sablatnig
  • Jason Gu
  • Stefan Mahlknecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


This paper presents landmark based global self-localization of autonomous mobile robots in a known but highly dynamic environment. The algorithm is based on range estimation to naturally occurring distinct features as it is not possible to modify the environment with special navigational aids. These features are sparse in our application domain and are frequently occluded by other robots. To enable the robot to estimate its absolute position with respect to a single landmark it is equipped with dead-reckoning sensors in addition to the stereo vision system mounted on a rotating head. The pivoted stereo vision system of the robot enables it to measure range and use bi/trilateration based methods as they require fewer landmarks compared to angle based triangulation. Further reduction of landmarks is achieved when robot orientation is estimated independently. Simulation results are presented which illustrate the performance of our algorithm.


Mobile Robot Landmark Point Robot Position Color Transition Global Localization 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chung, H., Ojeda, L., Borenstein, J.: Accurate mobile robot dead-reckoning with a precision-calibrated fiber-optic gyroscope. IEEE Transactions on Robotics and Automation 17, 80–84 (2001)CrossRefGoogle Scholar
  2. 2.
    Komoriya, K., Oyama, E.: Position estimation of a mobile robot using optical fiber gyroscope (ofg). In: International Conference on Intelligent Robots and Systems (IROS 1994), Munich, Germany, pp. 143–149 (1994)Google Scholar
  3. 3.
    Borenstein, J.: Experimental results from internal odometry error correction with the omnimate mobile robot. IEEE Transactions on Robotics and Automation 14, 963–969 (1998)CrossRefGoogle Scholar
  4. 4.
    Arsenio, A., Ribeiro, M.: Absolute localization of mobile robots using natural landmarks. In: Proceedings IEEE International Conference on Electronics, Circuits and Systems, vol. 2, pp. 483–486 (1998)Google Scholar
  5. 5.
    Sugihara, K.: Some location problems for robot navigation using a single camera. Computer Vision, Graphics, and Image Processing 42, 112–129 (1988)CrossRefGoogle Scholar
  6. 6.
    Yuen, D.C.K., MacDonald, B.: Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison. IEEE Transactions on Robotics 21, 217–226 (2005)CrossRefGoogle Scholar
  7. 7.
    Bais, A., Sablatnig, R.: Landmark based global self-localization of mobile soccer robots. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, p. 842. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Steinbauer, G., Bischof, H.: Illumination insensitive robot self-localization using panoramic eigenspaces. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 84–96. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Ji, J., Indiveri, G., Ploeger, P., Bredenfeld, A.: An omni-vision based self-localization method for soccer robot. In: IEEE symposium on Intelligent Vehicles (IV 2003), Columbus, Ohio, USA (2003)Google Scholar
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Weber, J., Franken, L., Jorg, K.W., Puttkamer, E.: Reference scan matching for global self-localization. Robotics and Autonomous Systems 40, 99–110 (2002)CrossRefGoogle Scholar
  14. 14.
    Clerentin, A., Delahoche, L., Pegard, C., Brassart, E.: A localization method based on two omnidirectional perception systems cooperation. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1219–1224 (2000)Google Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Borenstein, J., Everett, H.R., Feng, L.: Navigating Mobile Robots: Systems and Techniques. A. K. Peters, Ltd (1996)Google Scholar
  18. 18.
    Trucco, E., Verri, A.: Introductory TEchniques for 3-D Computer Vision. Prentice Hall, Upper Saddle River (1998)Google Scholar
  19. 19.
    Sutherland, K.T., Thompson, W.B.: Inexact navigation. In: IEEE International Conference on Robotics and Automation (ICRA 1993), pp. 1–7 (1993)Google Scholar
  20. 20.
    Clarke, J.C.: Modelling uncertainty: A primer. Technical Report 2161/98, University of Oxford, Dept. Engineering Science (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Abdul Bais
    • 1
    • 3
  • Robert Sablatnig
    • 2
  • Jason Gu
    • 3
  • Stefan Mahlknecht
    • 1
  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
  3. 3.Robotics Research Laboratory, Department of Electrical and Computer EngineeringDalhousie UniversityHalifaxCanada

Personalised recommendations