In the research on last mile automated driving, self-localization is an important problem to solve. In this paper, a precise self-localization algorithm is presented, which is based on a given map using LiDAR and camera sensors. The proposed approach is used as a solution for the localization problem within the VanAssist project. Several experiments were carriedout in order to validate the work and compare it to a reference and accurate RTK-GPS data. The evaluation shows that the localization result is within the requirements for last mile automated driving. Moreover, it indicates that the solution is robust to handle limitation in comparison to other approaches in the literature.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Precise self-localization for last mile delivery automated driving in unstructured environments


    Beteiligte:
    Czerwionka, Paul (Autor:in) / Pucks, Fabian (Autor:in) / Harte, Hans (Autor:in) / Blaschek, Roman (Autor:in) / Treiber, Robert (Autor:in) / Hussein, Ahmed (Autor:in)


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    1491033 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Last mile delivery system

    SALTER STUART C / WAAG ANDR ¨ / DAVID BRIAN GLICKMAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    LAST MILE DELIVERY SYSTEM

    SALTER STUART C / WAGH ARNAV / GLICKMAN DAVID BRIAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    A-VRPD: Automating Drone-Based Last-Mile Delivery Using Self-Driving Cars

    Imran, Navid Mohammad / Mishra, Sabyasachee / Won, Myounggyu | IEEE | 2023


    Autonomous Last-mile Delivery Vehicles in Complex Traffic Environments

    Li, Bai / Liu, Shaoshan / Tang, Jie et al. | ArXiv | 2020

    Freier Zugriff