In this paper, we develop and evaluate a Convolutional Neural Network (CNN)-based Light Detection and Ranging (LiDAR) localization algorithm that includes uncertainty quantification for ground vehicle navigation. This paper builds upon prior research where we used a CNN to estimate a rover’s position and orientation (pose) using LiDAR point clouds (PCs). This paper presents a simplification of the LiDAR PC processing and describes a new approach for outputting a covariance matrix in addition to the rover pose estimates. Performance assessment is carried out in a structured, static lab environment using a LiDAR-equipped rover moving along a fixed, repeated trajectory.


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    Titel :

    On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization


    Beteiligte:
    Joerger, Mathieu (Autor:in) / Wang, Julian (Autor:in) / Hassani, Ali (Autor:in)


    Erscheinungsdatum :

    05.06.2022


    Format / Umfang :

    1796967 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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