Simultaneous Localization and Mapping (SLAM) plays a significant role in providing accurate localization for intelligent vehicles. However, the traditional six-degree-of-freedom motion pose estimation assumption does not align with the actual vehicle motion, leading to pose estimation errors. To address this problem, we propose a stereo-visual SLAM algorithm specifically designed for road scenes to incorporate road constraints into the system. Our method leverages road constraints to eliminate errors caused by the six-degree-of-freedom motion assumption. Firstly, we represent the road as a set of discrete planes, simplifying the complex road surface into discrete local planes, and establishing the constraint relationship between camera poses and local planes. Then, we propose a tightly-coupled SLAM system that optimizes camera poses, map points, and discrete planes simultaneously. Finally, we evaluate the proposed system on the KITTI dataset and our self-collected dataset. Experimental results demonstrate that our method significantly improves the accuracy and stability of camera pose estimation.


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    Titel :

    DPC-SLAM: Discrete Plane Constrained VSLAM for Intelligent Vehicle in Road Environment


    Beteiligte:
    Wu, Zhihong (Autor:in) / Wang, Huaide (Autor:in) / An, Hao (Autor:in) / Zhu, Yuan (Autor:in) / Xu, Ruidong (Autor:in) / Lu, Ke (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    2442667 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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