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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    Title :

    On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization


    Contributors:


    Publication date :

    2022-06-05


    Size :

    1796967 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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