Large scale 3D maps constructed via LiDAR sensor are widely used on intelligent vehicles for localization in outdoor scenes. However, loading, communication and processing of the original dense maps are time consuming for onboard computing platform, which calls for a more concise representation of maps to reduce the complexity but keep the performance of localization. In this paper, we propose a teacher-student learning paradigm to compress the 3D point cloud map. Specifically, we first find a subset of LiDAR points with high number of observations to preserve the localization performance, which is regarded as the teacher of map compression. An efficient optimization strategy is proposed to deal with the massive data in original map. With the supervision of compressed map, a student model is built by training a random forest model fed with geometric feature descriptors of each point. As a result, the student model is able to compress the map without referring to the expensive numerical optimization. Additionally, by incorporating the features, the innovative student model can be generalized to other new maps while no re-training is required. We conduct thorough experiments on multi-session dataset and KITTI dataset to demonstrate the effectiveness and efficiency of the proposed learning paradigm, and the comparison with other map compression methods. The final results show that the learned student model can achieve efficient map compression with comparable LiDAR based localization performance to the original map at the same time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    3D LiDAR Map Compression for Efficient Localization on Resource Constrained Vehicles


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


    Erscheinungsdatum :

    01.02.2021


    Format / Umfang :

    7917071 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A SURVEY ON 3D LIDAR LOCALIZATION FOR AUTONOMOUS VEHICLES

    Elhousni, Mahdi / Huang, Xinming | British Library Conference Proceedings | 2020


    Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

    Miguel Paraiso, Miguel Ángel de / García Fernández, Fernando / Armingol Moreno, José María | BASE | 2020

    Freier Zugriff

    Localization of Intelligent Vehicles Based on LiDAR: A Review

    You, Xin / Mao, Peipei / Zhang, Hongbin et al. | British Library Conference Proceedings | 2020



    Map-based Localization for Automated Vehicles using LiDAR Features

    Hungar, Constanze | TIBKAT | 2021

    Freier Zugriff