Image-Based Monte-Carlo Localisation without a Map

  • Emanuele Menegatti
  • Mauro Zoccarato
  • Enrico Pagello
  • Hiroshi Ishiguro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2829)

Abstract

In this paper, we propose a way to fuse the image-based localisation approach with the Monte-Carlo localisation approach. The method we propose does not suffer of the major limitation of the two separated methods: the need of a metric map of the environment for the Monte-Carlo localisation and the failure of the image-based approach in environments with spatial periodicity (perceptual aliasing). The approach we developed exploits the properties of the Fourier Transform of the omnidirectional images and uses the similarity between the images to weights the beliefs about the robot position. Successful experiments in large indoor environment are presented in which we do not used a priory information on the metrical map of the environment.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aihara, H., Iwasa, N., Yokoya, N., Takemura, H.: Memory-based self-localisation using omnidirectional images. In: Jain, A.K., Venkatesh, S., Lovell, B.C. (eds.) Proc. of the 14th International Conference on Pattern Recognition, vol. I, pp. 1799–1803 (1998)Google Scholar
  2. 2.
    Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  3. 3.
    Burgard, W., Fox, D., Moors, M., Simmons, R., Thrun, S.: Collaborative multi-robot exploration. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE, Los Alamitos (2000)Google Scholar
  4. 4.
    Carpenter, J., Clifford, P., Fearnhead, P.: An improved particle filter for nonlinear problems. In: IEEE Proc. Radar, Sonar, Navigation vol. 146 (1999)Google Scholar
  5. 5.
    Cassinis, R., Duina, D., Inelli, S., Rizzi, A.: Unsupervised matching of visual landmarks for robotic homing unsing fourier-mellin transform. Robotics and Autonomous Systems 40(2-3) (August 2002)Google Scholar
  6. 6.
    F. Dellaert, D. Fox, W. Burgard, Thrun, S.: Monte Carlo Localization for Mobile Robots. In: IEEE International Conference on Robotics and Automation (ICRA 1999) (May 1999)Google Scholar
  7. 7.
    Fox, D.: KLD-Sampling: Adaptive Particle Filters. In: Advances in Neural Information Processing Systems 14 (NIPS). MIT Press, Cambridge (2001)Google Scholar
  8. 8.
    Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte Carlo Localization: Efficient Position Estimation for Mobile Robots. In: Proceedings of National Conference on Artificial intelligence AAAI (1999)Google Scholar
  9. 9.
    Gaspar, J., Winters, N., Santos-Victor, J.: Vision-based navigation and environmental representations with an omnidirectional camera. IEEE Transaction on Robotics and Automation 16(6) (December 2000)Google Scholar
  10. 10.
    Gutmann, J.-S., Burgard, W., Fox, D., Konolige, K.: An Experimental Comparisonof Localization Methods. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS) (October 1998)Google Scholar
  11. 11.
    Gutmann, J.-S., Fox, D.: An Experimental Comparison of Localization Methods Continued. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). da pubblicare (2002)Google Scholar
  12. 12.
    Ishiguro, H.: Development of low-cost compact omnidirectional vision sensors. In: Benosman, R., Kang, S. (eds.) Panoramic Vision,  ch. 3, pp. 23–38. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Ishiguro, H., Tsuji, S.: Image-based memory of environment. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1996), pp. 634–639 (1996)Google Scholar
  14. 14.
    Jogan, M., Leonardis, A.: Robust localization using panoramic view-based recognition. In: Proc. of the 15th Int. Conference on Pattern Recognition (ICPR 2000), vol. 4, pp. 136–139. IEEE Computer Society, Los Alamitos (2000)CrossRefGoogle Scholar
  15. 15.
    Kröse, B., Vlassis, N., Bunschoten, R., Motomura, Y.: A probabilistic model for appareance-based robot localization. Image and Vision Computing 19(6), 381–391 (2001)CrossRefGoogle Scholar
  16. 16.
    Menegatti, E., Nori, F., Pagello, E., Pellizzari, C., Spagnoli, D.: Designing an omnidirectional vision system for a goalkeeper robot. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup-2001: Robot Soccer World Cup V., pp. 78–87. Springer, Heidelberg (2002)Google Scholar
  17. 17.
    Menegatti, E., Pagello, E.: Omnidirectional mirror design based on the vision task. In: Proceeding of 1st Nat. Conf. Sistemi Autonomi Intelligenti e Robotica Avanzata, Frascati - Roma, pp. 151–159 (October 2002)Google Scholar
  18. 18.
    Menegatti, E., Zoccarato, M., Pagello, E., Ishiguro, H.: Hierarchical image-based localisation for mobile robots with monte-carlo localisation. In: Proc. of European Conf. on Mobile Robots (ECMR 2003) (September 2003) (to appear)Google Scholar
  19. 19.
    Pajdla, T., Hlaváč, V.: Zero phase representation of panoramic images for imagebased localization. In: Solina, F., Leonardis, A. (eds.) CAIP 1999. LNCS, vol. 1689, pp. 550–557. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Rizzi, A., Cassinis, R.: A robot self-localization system based on omnidirectional color images. Robotics and Autonomous Systems 34 (2001)Google Scholar
  21. 21.
    Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A., Fox, F.D., Haehnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: Probabilistic algorithms and the interactive museum tour-guide robot minerva. Intern. Journal of Robotics Research 19, 972–999 (2000)CrossRefGoogle Scholar
  22. 22.
    Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence Journal 128(1-2) (2001)Google Scholar
  23. 23.
    Wolf, J., Burgard, W., Burkhardt, H.: Robust vision-based localization for mobile robots using an image retrieval system based on invariant features. In: Proc. of the IEEE Intern. Conf. on Robotics & Automation (ICRA) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Emanuele Menegatti
    • 1
  • Mauro Zoccarato
    • 1
  • Enrico Pagello
    • 1
  • Hiroshi Ishiguro
    • 2
  1. 1.Intelligent Autonomous Systems LaboratoryDepartment of Information Engineering, The University of PaduaItaly
  2. 2.Department od Adaptive Machine SystemsOsaka UniversitySuita, OsakaJapan

Personalised recommendations