Among many components in automated driving, localization is one fundamental task that provides the context for scene understanding and motion planning. This contribution focuses on localization in high-definition (HD) maps, which provide detailed information of the driving environment. An important problem in localization is the data association (DA) between measurements and landmarks in the HD map. While other approaches mainly use geometric measurement information as well as the most likely DA hypothesis, this contribution proposes a localization algorithm capable of handling DA ambiguities using semantic information in a sliding window factor graph. By incorporating a max-mixture scheme, the algorithm is able to recover from potentially false estimations. Furthermore, a realistic simulation employing the CARLA simulator is used to generate controlled scenarios and evaluate the performance of the proposed algorithm. The experiments suggest that the proposed approach is able to achieve accurate and robust pose estimations in the presence of measurement uncertainties.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Semantic Landmark-based HD Map Localization Using Sliding Window Max-Mixture Factor Graphs


    Beteiligte:
    Stannartz, Niklas (Autor:in) / Liang, Jui-Lin (Autor:in) / Waldner, Mirko (Autor:in) / Bertram, Torsten (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    1348120 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Map-Based Localization with Factor Graphs for Automated Driving using Non-Semantic LiDAR Features

    Hungar, Constanze / Jurgens, Stefan / Wilbers, Daniel et al. | IEEE | 2020


    Shape correspondence through landmark sliding

    Song Wang, / Kubota, T. / Richardson, T. | IEEE | 2004


    Shape Correspondence through Landmark Sliding

    Wang, S. / Kubota, T. / Richardson, T. et al. | British Library Conference Proceedings | 2004


    Landmark-Based Vehicle Self-Localization Using Automotive Polarimetric Radars

    Weishaupt, Fabio / Tilly, Julius F. / Appenrodt, Nils et al. | IEEE | 2024


    VEHICLE INFRASTRUCTURE COOPERATIVE LOCALIZATION USING FACTOR GRAPHS

    Gulati, Dhiraj / Zhang, Feihu / Clarke, Daniel et al. | British Library Conference Proceedings | 2016