LiDAR is a key sensor commonly used in unmanned vehicles. Smog is a trouble for vehicle-mounted LiDAR when unmanned vehicles operates in actual road environments. It leads to a significant reduction in the ability of LiDAR-based scene understanding for them. Thus, it is essential to recognize the smog existing in the road scene quickly and accurately. This paper proposes a fine-grained point cloud smog segmentation network (SmogNet) for unmanned vehicles. We adopt an effective graph convolution kernel based on attention to extract features layer by layer. The key of SmogNet is two manual features we design specially to characterize the geometric features of smog in point cloud. We evaluate SmogNet in challenging real road scenes with simulated smog. It performs better than competitive methods and it can be effectively generalized. We use the Focal Loss during the training of the SmogNet and improve the problems caused by the imbalance of sample categories effectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SmogNet: A Point Cloud Smog Segmentation Network for Unmanned Vehicles


    Beteiligte:
    Tang, Hanbo (Autor:in) / Wu, Tao (Autor:in) / Dai, Bin (Autor:in)


    Erscheinungsdatum :

    29.10.2021


    Format / Umfang :

    3793485 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Improved Algorithm on Point Cloud Optimization for Unmanned Aerial Vehicles

    Yang, Nan ;Shao, Zhen Feng ;Guo, Bing Xuan | Trans Tech Publications | 2014


    Smog-treating catalyst

    Online Contents | 1995


    Beyond the Data Smog?

    Witlox, Frank | Online Contents | 2015