The characterization in real-time of the drivable space in front of the vehicle is a key issue for safe autonomous navigation or driving assistance. This paper presents a method that uses a lidar (a multilayer laser scanner) integrated in the front bumper of an automotive vehicle. A grid processing is first applied to detect and localize objects in the immediate environment after having compensated the movement of the vehicle. Accurate map information is then introduced in the perception scheme to refine the characterization of the drivable space. The paper details the different processing stages necessary to implement this method and presents the design of the system that has been prototyped on board an experimental vehicle. We report real experiments carried out in challenging urban environments to illustrate the performance of this approach which has been evaluated thanks to a precise retro-projection of the estimated drivable space in a wide-angle scene camera.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Drivable space characterization using automotive lidar and georeferenced map information


    Beteiligte:


    Erscheinungsdatum :

    01.06.2012


    Format / Umfang :

    1566994 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Drivable Space Characterization Using Automotive Lidar and Georeferenced Map Information

    Moras, J. / Rodriguez Florez, S.A. / Drevelle, V. et al. | British Library Conference Proceedings | 2012


    Drivable space alert

    ODINAEV KARINA / IEIZEROVITCH SHAY | Europäisches Patentamt | 2025

    Freier Zugriff

    Drivable Region Completion via a 3D LiDAR

    Jang, Wonje / Kim, Euntai | IEEE | 2024


    Off-Road Drivable Area Extraction Using 3D LiDAR Data

    Gao, Biao / Xu, Anran / Pan, Yancheng et al. | IEEE | 2019


    OFF-ROAD DRIVABLE AREA EXTRACTION USING 3D LIDAR DATA

    Gao, Biao / Xu, Anran / Pan, Yancheng et al. | British Library Conference Proceedings | 2019