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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    Title :

    Semantic 3D Grid Maps for Autonomous Driving


    Contributors:


    Publication date :

    2022-10-08


    Size :

    2747240 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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