Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles


    Beteiligte:
    Yin, Huan (Autor:in) / Tang, Li (Autor:in) / Ding, Xiaqing (Autor:in) / Wang, Yue (Autor:in) / Xiong, Rong (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    4209415 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    LOCNET: GLOBAL LOCALIZATION IN 3D POINT CLOUDS FOR MOBILE VEHICLES

    Yin, Huan / Tang, Li / Ding, Xiaqing et al. | British Library Conference Proceedings | 2018


    Three-Dimensional Object Co-Localization From Mobile LiDAR Point Clouds

    Guo, Wenzhong / Chen, Jiawei / Wang, Weipeng et al. | IEEE | 2021


    Point clouds registration system for autonomous vehicles

    HE RUNXIN / XIAO YONG / YUAN PENGFEI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Efficient rotation estimation for 3D registration and global localization in structured point clouds

    Ma, Yanxin / Guo, Yulan / Lei, Yinjie et al. | British Library Online Contents | 2017


    A POINT CLOUDS REGISTRATION SYSTEM FOR AUTONOMOUS VEHICLES

    HE RUNXIN / XIAO YONG / YUAN PENGFEI et al. | Europäisches Patentamt | 2021

    Freier Zugriff