This paper proposes a novel, automated algorithm for rapidly extracting urban road facilities, including street light poles, traffic signposts, and bus stations, for transportation-related applications. A detailed description and implementation of the proposed algorithm is provided using mobile laser scanning data collected by a state-of-the-art RIEGL VMX-450 system. First, to reduce the quantity of data to be handled, a fast voxel-based upward growing method is developed to remove ground points. Then, off-ground points are clustered and segmented into individual objects via Euclidean distance clustering and voxel-based normalized cut segmentation, respectively. Finally, a 3-D object matching framework, benefiting from a locally affine-invariant geometric constraint, is developed to achieve the extraction of 3-D objects. Quantitative evaluations show that the proposed algorithm attains an average completeness, correctness, quality, and F1-measure of 0.949, 0.971, 0.922, and 0.960, respectively, in extracting 3-D light poles, traffic signposts, and bus stations. Comparative studies demonstrate the efficiency and feasibility of the proposed algorithm for automated and rapid extraction of urban road facilities.


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    Title :

    Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data


    Contributors:
    Yu, Yongtao (author) / Li, Jonathan (author) / Guan, Haiyan (author) / Wang, Cheng (author)


    Publication date :

    2015-08-01


    Size :

    3109329 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English