Autonomous driving has gradually become a research hotspot in recent years. Visual Simultaneous Localization and Mapping (SLAM) technology can help unmanned vehicles accurately explore the environment at a lower cost, and the readability of the map can be improved by integrating target detection algorithms. However, the location and 3D shape of the object in the map were not obtained. The method of RGBD-SLAM based on object detection with two-stream YOLOv4-MobileNetv3 convolutional neural network is proposed in this paper. RGBD SLAM algorithm and target detection algorithm are combined to build an algorithm model that can generate the global sparse map and build target dense map quickly. The two-stream network is integrated to obtain 2D information about the target, and further combined with the camera pose after the front-end key frame detection of the SLAM algorithm in this paper, and the dense 3D point cloud of the target and the center point position of the object is obtained. Then, the sparse point cloud of the SLAM system and the dense point cloud of the target can be obtained. The experimental results show that the number of point clouds decreases by about 50% and the time for mapping accounts for about 60% of the global dense mapping time. The method of this paper can efficiently decrease the computational space and improve the speed of semantic mapping, which verifies its feasibility and superiority. It can be used to achieve large-area mapping and the ability to update maps during autonomous driving.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    RGBD-SLAM Based on Object Detection With Two-Stream YOLOv4-MobileNetv3 in Autonomous Driving


    Contributors:
    Li, Gongfa (author) / Fan, Hanwen (author) / Jiang, Guozhang (author) / Jiang, Du (author) / Liu, Yuting (author) / Tao, Bo (author) / Yun, Juntong (author)


    Publication date :

    2024-03-01


    Size :

    17212532 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Real‐time traffic cone detection for autonomous driving based on YOLOv4

    Qinghua Su / Haodong Wang / Min Xie et al. | DOAJ | 2022

    Free access

    Real‐time traffic cone detection for autonomous driving based on YOLOv4

    Su, Qinghua / Wang, Haodong / Xie, Min et al. | Wiley | 2022

    Free access

    Robust RGB-D SLAM for Dynamic Environments Based on YOLOv4

    Rong, Hanxiao / Ramirez-Serrano, Alex / Guan, Lianwu et al. | IEEE | 2020


    Design and Development of an Autonomous Car using Object Detection with YOLOv4

    Rishabh Chopda / Saket Pradhan / Anuj Goenka | BASE | 2022

    Free access

    SLAM in Autonomous Driving

    Ren, Jianfeng / Xia, Dong | Springer Verlag | 2023