Map Database Construction and Scene Prediction for Visual Navigation

  • M. Elarbi Boudihir
  • M. Dufaut
  • R. Husson
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)


In this paper an image is used to construct a map database for an autonomous navigation system. In fact, this map image looks like an aerial image taken at a certain altitude h. The level of the pixels belonging to a static object have been set to a value proportional to the object height. The aim of this method is to provide the vision system with what would almost be the 3-D scene appearance at any given point of the map specified by the navigation task. The construction method is fully detailed; moreover, the contribution of this work to the road edge selection algorithm is investigated and compared to the Hough transform based selection. This algorithm constitutes an efficient tool for object localization and identification, and a powerful support to the road edge selection task.


mobile robot visual navigation map construction scene prediction Hough transform 


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  1. [1]
    - M. Elarbi Boudihir, M. Dufaut, R. Husson. Guidage de robot mobile par détection de bord de route. Etude de la phase initiale de navigation. Revue d’Automatique et de Productique Appliquées. vol 3- n02/1990, p 67–81.Google Scholar
  2. [2]
    - Hebert. Martial. Building and navigating maps of road scenes using an active sensor IEEE International Conference of Robotics and Automation. Scottsdale 159 May 1989. p1136–1142.Google Scholar
  3. [3]
    - Tsutomo, M., Shin’ichi, Y. Autonomous navigation system for mobile robots using a route map. Advance Robotics Vol. 4 N0 3, pp. 243–261 VSP and Robotics Society of Japan 1990.Google Scholar
  4. [4]
    - Shigeo, H, Kazuhiro, Y, Yasumada, T. The study of map realization system: consideration of real time map generation. Advance Robotics Vol. 4 N0 3, pp. 223–242 VSP and Robotics Society of Japan 1990.Google Scholar
  5. [5]
    - Hebert, M., Caillas, C., Krotkov, E, Kweon, I.S., Kanade, T. Terrain mapping for a roving planetary explorer. IEEE International Conference on Robotics and Automation. Scottsdale 15–19 May 1989. p 997–1002.Google Scholar
  6. [6]
    - Illingworth, J, Kitler, J. The adaptative Hough transform. IEEE transactions on Pattern Analysis and Machine Intelligence. Vol. PAMI-9, N0.5, September 1987. pp 690–698.CrossRefGoogle Scholar
  7. [7]
    - Katani, K, Watanabe, K. Reconstruction of 3-D Road Geometry from images for autonomous land vehicles. IEEE Trans on Robot and Auto.Vol. 6 N01 February 1990. pp127–132.CrossRefGoogle Scholar
  8. [8]
    - Asada, M. Building a 3-D world model for a mobile robot from sensory data. Center for Automation Research Technical Report. CAR-TR-332, CS-TR- 1936, DACA 76–84-C-004. University of Maryland. 1987.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • M. Elarbi Boudihir
    • 1
  • M. Dufaut
    • 1
  • R. Husson
    • 1
  1. 1.CRAN - ENSEM, INPL. CNRS UA 821 CedexFrance

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