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Obstacles detection and localisation with 3D geographical model and monovision

Cindy Cappelle 1 Maan El Badaoui El Najjar 2, 3 François Charpillet 2 Denis Pomorski 3
1 LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 STF - Systèmes Tolérants aux Fautes
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In this paper, an obstacle detection approach for downtown environments is developed. This approach exploits a 3D geographical database managed by a 3D-GIS and a monovision-based system. The pose estimated by a LRK GPS is used to geo-localise the vehicle. After coordinates system conversion, the vehicle is localised in the 3D geographical database. An image processing module is developed to match synchronized images provided by 3D GIS and an on-board camera. Several kinds of obstacles are then detected and tracked by comparison between real images and virtual images. Finally, the distance between the camera and the obstacles is computed, as well as the geo-position of the detected obstacles. Experimental results with real data are presented in the final section.
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Conference papers
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https://hal.inria.fr/inria-00332460
Contributor : Cindy Cappelle <>
Submitted on : Tuesday, October 21, 2008 - 9:45:57 AM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM

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  • HAL Id : inria-00332460, version 1

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CNRS | INRIA | LAGIS | LORIA | UNIV-LORRAINE | CRISTAL | UNIV-LILLE

Citation

Cindy Cappelle, Maan El Badaoui El Najjar, François Charpillet, Denis Pomorski. Obstacles detection and localisation with 3D geographical model and monovision. IEEE Intelligent Transportation Systems Conference - ITSC'07, Sep 2007, Seattle, United States. pp.1102-1107. ⟨inria-00332460⟩

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