Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds’ contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving


    Beteiligte:
    Shi, Chenghao (Autor:in) / Chen, Xieyuanli (Autor:in) / Lu, Huimin (Autor:in) / Deng, Wenbang (Autor:in) / Xiao, Junhao (Autor:in) / Dai, Bin (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-10-01


    Format / Umfang :

    8864834 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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

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


    3D LIDAR Point Cloud Based Intersection Recognition for Autonomous Driving

    Zhu, Q. / Chen, L. / Li, Q. et al. | British Library Conference Proceedings | 2012


    3D LIDAR point cloud based intersection recognition for autonomous driving

    Zhu, Quanwen / Chen, Long / Li, Qingquan et al. | IEEE | 2012


    Index Coding of Point Cloud-Based Road Map Data for Autonomous Driving

    Chu, Kai Fung / Magsino, Elmer R. / Ho, Ivan Wang-Hei et al. | IEEE | 2017


    Self-Supervised Point Cloud Registration With Deep Versatile Descriptors for Intelligent Driving

    Liu, Dongrui / Chen, Chuanchaun / Xu, Changqing et al. | IEEE | 2023