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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    Title :

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


    Contributors:
    Zai, Dawei (author) / Li, Jonathan (author) / Guo, Yulan (author) / Cheng, Ming (author) / Lin, Yangbin (author) / Luo, Huan (author) / Wang, Cheng (author)


    Publication date :

    2018-03-01


    Size :

    5848335 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English