The perceptual information acquired by a single vehicle-side LiDAR in autonomous driving is limited, and this phenomenon is more prominent at intersections where vehicles are turning. Existing solutions improve vehicle perception by designing complex systems to match homogeneous point clouds acquired by the same type of sensors. In this study, we propose a heterogeneous point cloud registration for vehicle-infrastructure collaboration (HPCR-VI) that supplements the missing sensory information of the vehicle-side mechanical LiDAR with the point cloud information acquired by the infrastructure-side solid-state LiDAR. The HPCR-VI framework proposed in this paper breaks the limitation of homogeneous point cloud registration and can quickly obtain alignment results from two frames of heterogeneous point clouds, whose densities and viewing angles differ greatly, solving the heterogeneous point cloud registration problem where traditional point cloud alignment methods fail. Our proposed method is tested on the DAIR-V2X dataset, and the success rate of alignment is 40-50 points higher than that of the baseline method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    HPCR-VI: Heterogeneous point cloud registration for vehicle-infrastructure collaboration


    Contributors:
    Zhao, Yuting (author) / Zhang, Xinyu (author) / Zhang, Shiyan (author) / Qiu, Shaoting (author) / Yin, Haojie (author) / Zhang, Xu (author)


    Publication date :

    2023-06-04


    Size :

    4247206 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    An improved iterative nearest point registration method for vehicle-mounted laser point cloud

    Yu, Zhou / Sun, Bingyu | British Library Conference Proceedings | 2023


    HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration

    Tang, Canhui / Li, Yiheng / Du, Shaoyi et al. | IEEE | 2023


    Design Hybrid Computing Architecture for Accelerating Point Cloud Registration

    Wang, Xiao / Deng, Xiaodong / Li, Yingxiang et al. | IEEE | 2023


    Global-PBNet: A Novel Point Cloud Registration for Autonomous Driving

    Zheng, Yuchao / Li, Yujie / Yang, Shuo et al. | IEEE | 2022


    Driving infrastructure value through collaboration

    Candfield, Jeremy / Featherstone, Steve / McLloughlin, David | IuD Bahn | 2012