Effective extraction of road boundaries plays a significant role in intelligent transportation applications, including autonomous driving, vehicle navigation, and mapping. This paper presents a new method to automatically extract 3-D road boundaries from mobile laser scanning (MLS) data. The proposed method includes two main stages: supervoxel generation and 3-D road boundary extraction. Supervoxels are generated by selecting smooth points as seeds and assigning points into facets centered on these seeds using several attributes (e.g., geometric, intensity, and spatial distance). 3-D road boundaries are then extracted using the $\alpha $ -shape algorithm and the graph cuts-based energy minimization algorithm. The proposed method was tested on two data sets acquired by a RIEGL VMX-450 MLS system. Experimental results show that road boundaries can be robustly extracted with an average completeness over 95%, an average correctness over 98%, and an average quality over 94% on two data sets. The effectiveness and superiority of the proposed method over the state-of-the-art methods is demonstrated.


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    Titel :

    3-D Road Boundary Extraction From Mobile Laser Scanning Data via Supervoxels and Graph Cuts


    Beteiligte:
    Zai, Dawei (Autor:in) / Li, Jonathan (Autor:in) / Guo, Yulan (Autor:in) / Cheng, Ming (Autor:in) / Lin, Yangbin (Autor:in) / Luo, Huan (Autor:in) / Wang, Cheng (Autor:in)


    Erscheinungsdatum :

    2018-03-01


    Format / Umfang :

    5848335 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch