Simultaneous localization and mapping (SLAM), as an important tool for vehicle positioning and mapping, plays an important role in the unmanned vehicle technology. This paper mainly presents a new solution to the LIDAR-based SLAM for unmanned vehicles in the off-road environment. Many methods have been proposed to solve the SLAM problems well. However, in complex environment, especially off-road environment, it is difficult to obtain stable positioning results due to the rough road and scene diversity. We propose a SLAM algorithm based on grid which combining probability and feature by Expectation-maximization (EM). The algorithm is mainly divided into three steps: data preprocessing, pose estimation, updating feature grid map. Our algorithm has strong robustness and real-time performance. We have tested our algorithm with our datasets of the multiple off-road scenes which obtained by LIDAR. Our algorithm performs pose estimation and feature map updating in parallel, which guarantees the real-time performance of the algorithm. The average processing time of each frame is about 55ms, and the average relative translation error is around 0.94%. Compared with several state-of-the-art algorithms, our algorithm has better performance in robustness and location accuracy.


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    Titel :

    CPFG-SLAM:a Robust Simultaneous Localization and Mapping based on LIDAR in Off-Road Environment


    Beteiligte:
    Ji, Kaijin (Autor:in) / Chen, Huiyan (Autor:in) / Di, Huijun (Autor:in) / Gong, Jianwei (Autor:in) / Xiong, Guangming (Autor:in) / Qi, Jianyong (Autor:in) / Yi, Tao (Autor:in)


    Erscheinungsdatum :

    01.06.2018


    Format / Umfang :

    4187214 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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