Highly accurate self-localization is necessary to achieve safe autonomous driving. Recently, map-based self-localization has been attracting attention. Maps generally consist of point cloud information and vector information. However, this information is managed separately and is not compatible. Therefore, we propose a map format that bridges point cloud information and vector information. Vector data is extracted for objects in the environment and associated with a normal distribution representing detailed geometric information. The proposed map can be used as a vector map for various tasks in autonomous driving, and the set of normal distributions provides accurate self-localization. Experiments show that the proposed self-localization achieves lane-level accuracy (~25 cm location) in urban Tokyo.


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    Titel :

    Integrated High-Definition Maps for Self-Localization with Vector and Polygon Data


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


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    674400 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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