LiDAR-based perception systems have become widely adopted in autonomous vehicles. However, their performance can be severely degraded in adverse weather conditions, such as rain, snow or fog. To address this challenge, we propose a method for improving the robustness of LiDAR-based perception in adverse weather, using data augmentation techniques on point clouds. We use novel as well as established data augmentation techniques, such as realistic weather simulations, to provide a wide variety of training data for LiDAR-based object detectors. The performance of the state-of-the-art detector Voxel R-CNN using the proposed augmentation techniques is evaluated on a data set of real-world point clouds collected in adverse weather conditions. The achieved improvements in average precision (AP) are 4.00 p.p. in fog, 3.35 p.p. in snow, and 4.87 p.p. in rain at moderate difficulty. Our results suggest that data augmentations on point clouds are an effective way to improve the robustness of LiDAR-based object detection in adverse weather.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Enhancing Robustness of LiDAR-Based Perception in Adverse Weather using Point Cloud Augmentations


    Beteiligte:
    Teufel, Sven (Autor:in) / Gamerdinger, Jorg (Autor:in) / Volk, Georg (Autor:in) / Gerum, Christoph (Autor:in) / Bringmann, Oliver (Autor:in)


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    984192 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Survey on LiDAR Perception in Adverse Weather Conditions

    Dreissig, Mariella / Scheuble, Dominik / Piewak, Florian et al. | IEEE | 2023



    Navigating on Adverse Weather: Enhancing LiDAR-Based Detection with the DBSPRY Dataset

    Granado, Diogo Wachtel / Trevisol, Heitor Derder / Rothmeier, Thomas et al. | IEEE | 2024


    BloomNet: Perception of Blooming Effect in ADAS using Synthetic LiDAR Point Cloud Data

    Uttarkabat, Satarupa / Appukuttan, Sarath / Gupta, Kwanit et al. | IEEE | 2024


    YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

    Munir, Adnan / Siddiqui, Abdul Jabbar / Hossain, M. Shamim et al. | IEEE | 2025