In autonomous driving, achieving self-localization at the lane level is critical for various tasks, such as control and scene understanding. Particularly in urban areas, map-based methods have become the standard in recent years due to their stability. However, there are locations where map-based self-localization potentially performs poorly. Therefore, it is important to identify such locations in advance for a safe autonomous driving system. Existing methods for the prior evaluation of self-localization rely on prior experiments conducted in various settings. This paper proposes an evaluation metric focusing on an autocorrelation function of map structures, which is more intrinsic than existing evaluation methods. Specifically, we focus on the condition number of the autocorrelation function's shape to model the difficulty of identifying the true pose in the function. An experiment conducted in real urban environments revealed the presence of common areas where certain self-localization algorithms tend to fail. Furthermore, the experiment demonstrated a statistically significant difference in the proposed criterion between locations with high and low errors in self-localization, suggesting that criterion values can be used to identify areas with higher errors in self-localization.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-Localization Error Evaluation from Map Structure Based on Autocorrelation Values


    Beteiligte:
    Endo, Yuki (Autor:in) / Kamijo, Shunsuke (Autor:in)


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    1048745 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Pre-Estimating Self-Localization Error of NDT-Based Map-Matching From Map Only

    Javanmardi, Ehsan / Javanmardi, Mahdi / Gu, Yanlei et al. | IEEE | 2021


    LOCALIZATION ERROR MONITORING

    ADAMS DEREK / BERMAN DAVID BURDICK / BOSSE MICHAEL CARSTEN et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    LOCALIZATION ERROR MONITORING

    ADAMS DEREK / BERMAN DAVID BURDICK / BOSSE MICHAEL CARSTEN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Localization error monitoring

    ADAMS DEREK / BERMAN DAVID BURDICK / BOSSE MICHAEL CARSTEN et al. | Europäisches Patentamt | 2022

    Freier Zugriff