Autonomous vehicle self-positioning based on 3D light detection and ranging (Lidar) has become popular recently due to disadvantages of global navigation satellite system (GNSS) in urban areas. As LiDAR-based simultaneous localization and mapping (SLAM) methods suffer from error accumulation, state-of-the-art approaches match the point cloud data acquired by LiDAR to the priori known 3D point cloud map to obtain the position of the vehicle within the map. However, 3D point cloud map is very expensive to store and download as it contains an enormous amount of data even for a small area (around 300 million points per km2). In this study, rather than using 3D point cloud directly as a map, we focused on the planar surfaces which are mostly available in urban areas, easy to extract, and at the same time clearly observable by LiDAR. Therefore, in our proposed map, we extract the planar surfaces from the 3D point cloud and calculate its uncertainty (deviation) and store them as a prior map. Accordingly, in this map, we can abstract several thousands of points by only one plane. As a result, we can extremely shrink the map size (25 million points to around 1000 planes). Later in the localization phase, we reconstruct Gaussian mixture model for each planar surface based on previously stored deviation, and match LiDAR data to it to obtain the precise location of the vehicle. Experiments conducted in one of the urban areas of Tokyo show that even though we extremely shrank the map size, we could preserve the mean error of the localization less than 43cm comparing to other point cloud based methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Autonomous vehicle self-localization based on probabilistic planar surface map and multi-channel LiDAR in urban area


    Beteiligte:
    Javanmardi, Ehsan (Autor:in) / Javanmardi, Mahdi (Autor:in) / Gu, Yanlei (Autor:in) / Kamijo, Shunsuke (Autor:in)


    Erscheinungsdatum :

    2017-10-01


    Format / Umfang :

    3231473 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

    Miguel Paraiso, Miguel Ángel de / García Fernández, Fernando / Armingol Moreno, José María | BASE | 2020

    Freier Zugriff

    LiDAR-Based Urban Autonomous Platooning Simulation

    Alzu'bi, Hamzeh / Tasky, Tom | SAE Technical Papers | 2020


    LIDAR BASE, LIDAR DEVICE AND AUTONOMOUS VEHICLE

    REN RUI / FAN ZONGTAO / WANG BOLEI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Autonomous vehicle LIDAR mirror

    HUEBNER ANNETTE LYNN / DELLOCK PAUL KENNETH / SALTER STUART C et al. | Europäisches Patentamt | 2020

    Freier Zugriff