Global localization in 3D point clouds is a challenging task for mobile vehicles in outdoor scenarios, which requires the vehicle to localize itself correctly in a given map without prior knowledge of its pose. This is a critical component of autonomous vehicles or robots on the road for handling localization failures. In this paper, based on reduced dimension scan representations learned from neural networks, a solution to global localization is proposed by achieving place recognition first and then metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted feature learning method for 3D Light detection and ranging (LiDAR) point clouds using artificial statistics and siamese network, which transforms the place recognition problem into a similarity modeling problem. Additionally, the sensor data using dimension reduced representations require less storage space and make the searching easier. With the learned representations by networks and the global poses, a prior map is built and used in the localization framework. In the localization step, position only observations obtained by place recognition are used in a particle filter algorithm to achieve precise pose estimation. To demonstrate the effectiveness of our place recognition and localization approach, KITTI benchmark and our multi-session datasets are employed for comparison with other geometric-based algorithms. The results show that our system can achieve both high accuracy and efficiency for long-term autonomy.


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    Titel :

    3D LiDAR-Based Global Localization Using Siamese Neural Network


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


    Erscheinungsdatum :

    01.04.2020


    Format / Umfang :

    3401687 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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