Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is three-dimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.


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    Titel :

    Semantic 3D Grid Maps for Autonomous Driving


    Beteiligte:
    Khoche, Ajinkya (Autor:in) / Wozniak, Maciej K (Autor:in) / Duberg, Daniel (Autor:in) / Jensfelt, Patric (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    2747240 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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