In this work, a simple yet effective deep neural network is proposed to generate the dense depth map of the scene by exploiting both LiDAR sparse point cloud and the monocular camera image. Specifically, a feature pyramid network is firstly employed to extract feature maps from images across time. Then the relative pose is calculated by minimizing the feature distance between aligned pixels from inter-frame feature maps. Finally, the feature maps and the relative pose are further applied to compute the feature-metric loss for training the depth completion network. The key novelty of this work lies in that a self-supervised mechanism is presented to train the depth completion network by directly using visual-LiDAR odometry between consecutive frames. Comprehensive experiments and ablation studies on benchmark dataset KITTI demonstrate the superior performance over other state-of-the-art methods in terms of pose estimation and depth completion. The detailed performance of the proposed approach (referred to as SelfCompDVLO) can be found on the KITTI depth completion benchmark. The source code, models, and data have been made available at GitHub.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving


    Beteiligte:
    Song, Zhenbo (Autor:in) / Lu, Jianfeng (Autor:in) / Yao, Yazhou (Autor:in) / Zhang, Jian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.08.2022


    Format / Umfang :

    5583079 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Visual Odometry Integrated Semantic Constraints towards Autonomous Driving

    Lan, FengChong / Chen, Jiqing / Yao, Siyu | SAE Technical Papers | 2022


    HETEROGENEOUS MULTI-THREADED VISUAL ODOMETRY IN AUTONOMOUS DRIVING

    SHAO WEIZHAO / JAIN ANKIT KUMAR / XINJILEFU XXX et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Visual Odometry Integrated Semantic Constraints towards Autonomous Driving

    Yao, Siyu / Lan, FengChong / Chen, Jiqing | British Library Conference Proceedings | 2022


    LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving

    Fu, Chen / Mertz, Christoph / Dolan, John M. | IEEE | 2019


    Self-supervised 4-D Radar Odometry for Autonomous Vehicles

    Zhou, Huanyu / Lu, Shouyi / Zhuo, Guirong | IEEE | 2023