Traversable regions identification technology plays a crucial role in ensuring safe driving for unmanned ground vehicles in off-road environments. However, the unstructured terrain makes it challenging to identify traversable regions. To enhance the safety of off-road driving, a LiDAR-based traversable regions identification method is proposed in this paper. Firstly, a deep learning-based neural network is used to segment the traversable regions, obstacles, and vegetation. Next, an improved Gaussian Process(GP)-based modeling method is designed to model the traversable regions with a leading speed, and the obstacle point clouds are refined with a composite filter. Finally, field experiments have demonstrated that our proposed scheme outperforms existing state-of-the-art (SOTA) traditional and deep-learning-based methods in accurately identifying both road regions and obstacles, with precision improvements of up to 14% and recall improvements of up to 9%.


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    Titel :

    LiDAR Based Traversable Regions Identification Method for Off-Road UGV Driving


    Beteiligte:
    Shan, Yunxiao (Autor:in) / Fu, Yao (Autor:in) / Chen, Xiangchun (Autor:in) / Lin, Hongquan (Autor:in) / Zhang, Ziquan (Autor:in) / Lin, Jun (Autor:in) / Huang, Kai (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.02.2024


    Format / Umfang :

    7434698 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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