LiDAR odometry (LO) has gained popularity in recent years due to accurate depth measurement and robustness to illumination. Typically, the solutions based on scan-to-map matching mainly optimize current pose. To further reduce the accumulated error of pose estimation, the fixed-lag smoothing that optimizes fixed-size poses simultaneously by matching corresponding point features of multiple frames becomes necessary. The integration of fixed-lag smoothing with LO still needs further exploration. In this paper, a general fixed-lag smoothing module is proposed, which can be appended to existing LO framework to improve the consistency of trajectory. Also, a fast scan-to-map matching module based on sparse features is developed to guarantee the real-time performance. Besides, the feature-centric feature management strategy is adopted in both scan-to-map matching and fixed-lag smoothing modules, which makes the proposed LO efficient. On this basis, a hierarchical estimation-based LiDAR odometry is presented, where low-level scan-to-map matching estimates pose of each frame by aligning associated features in the frame and corresponding surrounding map with high efficiency, and high-level fixed-lag smoothing further optimizes keyframe poses in a sliding window by matching associated features among multiple frames with high accuracy. As a result, a fast and accurate pose estimation is achieved, which is verified by experiments on the KITTI dataset, Newer College dataset, and an actual outdoor scenario.


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    Titel :

    Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing


    Beteiligte:
    Liang, Shuang (Autor:in) / Cao, Zhiqiang (Autor:in) / Wang, Chengpeng (Autor:in) / Yu, Junzhi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-02-01


    Format / Umfang :

    3529608 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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