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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    Title :

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


    Contributors:


    Publication date :

    2021-07-11


    Size :

    2869847 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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