This paper proposes a new method to extract background and segment targets from point clouds collected by three-dimensional roadside LiDAR in snowfall weather. Background point extraction and target segmentation are two main problems in environmental perception based on roadside LiDAR. This paper first introduces a new background filtering algorithm, which uses the historical point cloud sequence to construct the background model in real time and filter the background by background difference. Then, a non-background target segmentation algorithm is proposed, including a linear density filter for filtering snow noise and a hierarchical target clustering algorithm at the beam level, which effectively realizes the accurate detection of road targets affected by snow occlusion and avoids over-segmentation and under-segmentation. Finally, the performance of our method is compared with that of the commonly used background extraction and target segmentation methods by using the data collected in the snowfall environment by our intelligent roadside system. The results show that the target detection accuracy rate and recall rate of our method can reach 95.5% and 96.7%, respectively, which are better than those of the existing methods, and can be more accurate and effective to segment road targets from the snowfall cloud.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Background Extraction and Objects Segmentation with 3D Roadside LiDAR under Snowy Weather


    Contributors:


    Publication date :

    2022-10-08


    Size :

    863397 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A combined denoising algorithm for roadside LiDAR point clouds under snowy condition

    Lu, Quanli / Lan, Xiaowei / Xu, Jiabin et al. | British Library Conference Proceedings | 2022


    Reheating of switches during snowy weather

    Puget / Neumann | Engineering Index Backfile | 1948


    Automatic Background Filtering Method for Roadside LiDAR Data

    Wu, Jianqing / Xu, Hao / Sun, Yuan et al. | Transportation Research Record | 2018


    Automatic Background Filtering for Cooperative Perception Using Roadside LiDAR

    Liu, Jianqi / Zhao, Jianguo / Guo, Junfeng et al. | IEEE | 2024


    Azimuth-Height Background Filtering Method for Roadside LiDAR Data

    Zhao, Junxuan / Xu, Hao / Xia, Xueting et al. | IEEE | 2019