Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying first- and second-order uncertainty and which does not rely on manually labeled data. Results on synthetic and on real-world data show superiority over other approaches. Source code and datasets are available at https://github.com/ika-rwth-aachen/EviLOG.


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    Titel :

    A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping


    Beteiligte:
    Van Kempen, Raphael (Autor:in) / Lampe, Bastian (Autor:in) / Woopen, Timo (Autor:in) / Eckstein, Lutz (Autor:in)


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    2869847 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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