We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDAR and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDAR and stereo camera (stereocam) data recorded in real traffic scenarios. Our method estimates cell occupancy robustly and with a high level of detail while maximizing efficiency and minimizing the dependency to external processing modules.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Dual Evidential Top-View Representation to Model the Semantic Environment of Automated Vehicles


    Contributors:

    Published in:

    Publication date :

    2024-01-01


    Size :

    3204915 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Online Road Model Generation From Evidential Semantic Grids

    Thomas, Julian / Tatsch, Julian / Knoll, Alois et al. | IEEE | 2020


    Evidential deep learning-based multi-modal environment perception for intelligent vehicles

    Geletu, Mihreteab Negash / Giurgi, Danut-Vasile / Josso-Laurain, Thomas et al. | IEEE | 2023


    Stereo Vision for Dynamic Urban Environment Perception Using Semantic Context in Evidential Grid

    Giovani, Bernardes Vitor / Victorino, Alessandro Correa / Ferreira, Janito Vaqueiro | IEEE | 2015


    Semantic evidential lane grids with prior maps for autonomous navigation

    Chunlei Yu / Cherfaoui, Veronique / Bonnifait, Philippe | IEEE | 2016