Simultaneous Location and Mapping (SLAM) is a common algorithm for position estimation in GNSS-denied environments. However, the high structural consistency and low lighting conditions in tunnel environments pose challenges for traditional visual SLAM and LiDAR SLAM. To this end, this paper presents LiDAR and optical flow fusion odometry (LOFF), which uses a direction-separated data fusion method to fuse optical flow odometry into the degenerate direction of the LiDAR SLAM without sacrificing the accuracy. Moreover, LOFF incorporates detectors and a compensator, allowing for a smooth transition between general environments and degeneracy environments. This capability facilitates the stable flight of unmanned aerial vehicles (UAVs) in GNSS-denied tunnel environments, including corners and long-distance consistency. Through real-world experiments conducted in a GNSS-denied pedestrian tunnel, we demonstrate the superior position accuracy and trajectory smoothness of LOFF compared to state-of-the-art visual SLAM and LiDAR SLAM.


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    Title :

    LOFF: LiDAR and Optical Flow Fusion Odometry


    Contributors:
    Junrui Zhang (author) / Zhongbo Huang (author) / Xingbao Zhu (author) / Fenghe Guo (author) / Chenyang Sun (author) / Quanxi Zhan (author) / Runjie Shen (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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