Steps Towards One-Shot Vision-Based Self-Localization

  • B. Raducanu
  • P. Sussner
  • M. Graña
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 109)


Approaches to the problem of self-localization are based on odometry (dead-reckoning) and the matching of feature vectors obtained from sensor readings, captured at selected positions and orientations (landmarks) during map construction. Due to the big uncertainties of sensor inputs, the matching of their readings is taken as a correction for odometry-based self-localization. Vision-based self-localization is based on the recognition of landmarks or the matching of 3D information extracted from the images. Such approaches are computationally expensive. We propose the use of Heteroassociative Morphological Memories for the fast recognition of views that can be used in a one-shot recognition procedure, inspired in the instantaneous recognition of specific views of an environment where an autonomous agent tries to obtain orientation. Recognition of world views provide inmediate confirmation or refutation of the position hypithesis that other senses or the dead-reckoning procedures may sustain. The present status of our work addresses the problem of significative shot identification.


Mobile Robot Training Sequence Perfect Recall Correlation Base Distance Original Gray Scale Image 
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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  1. 1.
    Balkenius, C. and L. Kopp. Robust self-localization using elastic templates. In T. Lindberg (ed.), Proceedings of Swedish Symposium on Image Analysis, 1997Google Scholar
  2. 2.
    Brooks, R. A. Achieving artificial intelligence through building robots. AI MEMO 899, AI Lab, MIT, 1986Google Scholar
  3. 3.
    Brooks, R. A. Intelligence without reason. Proc. of International Joint Conference on Artificial Intelligence, pp. not available, Sydney, Australia, 1991Google Scholar
  4. 4.
    Brooks, R. A. Intelligence without representation. Artificial Intelligence Journal, 47: 139–159, 1991CrossRefGoogle Scholar
  5. 5.
    Chatila, R. Deliberation and reactivity in autonomous mobile robots. Robotics and Autonomous Systems, 16: 197–211, 1995CrossRefGoogle Scholar
  6. 6.
    Colios, C. I. and P. E. Trahanias. A framework for visual landmark identification based on projective and point-permutation invariant vectors. Robotics and Autonomous Systems, 35: 37–51, 2001MATHCrossRefGoogle Scholar
  7. 7.
    Connell, J. H. SSS:A hybrid architecture applied to robot navigation. Proc. of IEEE Conference on Robotics and Automation, pp. 2719–1724, 1992Google Scholar
  8. 8.
    Crowley, J. L. Navigation for an intelligent mobile robot.. IEEE Journal of Robotics and Automation, pp. 31–41, 1985Google Scholar
  9. 9.
    Crowley, J. L. Mathematical foundation of navigation and perception for an autonomous mobile robot. In L. Dorst (ed.), Reasoning with Uncertainty in Robotics, Springer-Verlag, 1996Google Scholar
  10. 10.
    Fox, D. Markov Localization: A Probabilistic Framework for Mobile Robot Localization and Navigdation. Ph. D. Thesis, University of Bonn, Germany, December 1998Google Scholar
  11. 11.
    Franz, M. O., B. Schölkopf, H. A. Mallot and H. H. Bülthoff. Where did I take this snapshot? Scene-based homing by image matching. Biological Cybernetics, 79: 191–202, 1998MATHCrossRefGoogle Scholar
  12. 12.
    Gader, P.D., M. A. Khabou, and A. Koldobsky. Morphological regularization neural networks. Pattern Recognition, 33: 935–944, 2000CrossRefGoogle Scholar
  13. 13.
    Giralt G., R. Chatila and M. Vaisset. An integrated navigation and motion control system for autonomous multisensory mobile robots. Robotics Research, Brady and Paul (eds.), MIT PRESS, pp. 191-214, 1984Google Scholar
  14. 14.
    Gomi, T. Non-Cartesian robotics. Robotics and Autonomous Systems, 18: 169184, 1996Google Scholar
  15. 15.
    Gomi, T. and K. Ide. Emulation of emotion using vision with learning. Proc. of International Conference on Intelligent Robots and Systems, pp. not available, Munich, Germany, 1994Google Scholar
  16. 16.
    Jackway, P. T and M. Deriche. Scale-space properties of the multiscale morphological dilation-erosion. IEEE Trans. on Pattern Recognition and Machine Intelligence, 18 (1): 38–51, 1996CrossRefGoogle Scholar
  17. 17.
    Livatino, S. and C. Madsen. Optimization of robot self-localization accuracy by automatic visual-landmark selection. Proceedings of 11th Scabdinavian Conference on Image Analysis (SCIA), pp. 501–506, 1999Google Scholar
  18. 18.
    Mondada, F., E. Franzi and P. Ienne. Mobile robot miniaturization: A tool for investigation in control algorithms. Proc. of Third International Symposium on Experimental Robotics, pp. not available, Kyoto, Japan, 1993Google Scholar
  19. 19.
    Olson, C. F. Mobile Robot self-localization by iconic matching of range maps. Proceedings of the 8th International Conference on Advanced Robotics (ICAR), pp. 447–452, 1997Google Scholar
  20. 20.
    Pessoa, L. F. C. and P. Maragos. Neural networks with hybrid morphological/rank /linear nodes: A unifying framework with applications to handwritten character recognition. Pattern Recognition, 33: 945–960, 2000CrossRefGoogle Scholar
  21. 21.
    Reuter, J. Mobile robot self-localization using PDAB. Proceedings of International Conference on Robotics and Automation (ICRA), pp. not available, 2000Google Scholar
  22. 22.
    Ritter, G. X., P. Sussner and J.L. Diaz de Leon. Morphological Associative Memories. IEEE Transactions on Neural Networks, 9 (2): 281–292, 1998CrossRefGoogle Scholar
  23. 23.
    Ritter, G. X. and P. Sussner. An introduction to Morphological Neural Networks. Proc. of Intl Conference on Pattern Recognition, pp. 709–717, 1996Google Scholar
  24. 24.
    Ritter, G. X., J. L. Diaz-de-Leon and P. Sussner. Morphological Bidirectional Associative Memories. Neural Networks, 12: 851–867, 1999CrossRefGoogle Scholar
  25. 25.
    Safiiotti, A. and L. P. Wesley. Perception-based self-localization using fuzzy location. In L. Dorst, M. Van Lambalgen and F. Voorbraak (eds.), Lecture Notes in Artificial Intelligence 1093, Springer-Verlag, pp. 368–385, 1996Google Scholar
  26. 26.
    Steels, L. Building agents out of autonomous behavior systems. The Biology and Technology of Intelligent Autonomous Agents, NATO Advanced Study Institute, Lecture Notes, Trento, 1993Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • B. Raducanu
    • 1
  • P. Sussner
    • 1
  • M. Graña
    • 1
  1. 1.Dept. CCIAUPV/EHUSan SebastianSpain

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