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    ASIA unversity > 資訊學院 > 資訊傳播學系 > 期刊論文 >  Item 310904400/4378

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4378

    Title: Vision-based autonomous land vehicle guidance in outdoor environments using combined line and road following techniques
    Authors: K. H. Chen;W. H. Tsai
    Contributors: Department of Information Communication
    Date: 1997-05
    Issue Date: 2009-11-25 10:31:07 (UTC+8)
    Publisher: Asia University
    Abstract: An intelligent approach to autonomous land vehicle (ALV) guidance in outdoor road environments using combined line and road following and color information clustering techniques is proposed. Path lines and road boundaries are selected as reference models, called the line-model and the road-model, respectively. They are used to perform line-model matching (LMM) and road-model matching (RMM) to locate the ALV for line and road following, respectively. If there are path lines in the road, the LMM process is used to locate the ALV because it is faster than the RMM process. On the other hand, if no line can be found in the road, the RMM process is used. To detect path lines in a road image, the Hough transform is employed, which does not take much computing time because bright pixels in the road are very few. Various color information on roads is used for extracting path lines and road surfaces. And the ISODATA clustering algorithm based on an initial-center-choosing technique is employed to solve the problem caused by great changes of intensity in navigations. The double model matching procedure combined with the color information clustering process enables the ALV to navigate smoothly in roads even if there are shadows, cars, people, or degraded regions on roadsides. Some intelligent methods to speed up the model matching processes and the Hough transform based on the feedback of the previous image information are also presented. Successful navigations show that the proposed approach is effective for ALV guidance in common roads.
    Relation: Journal of Robotic Systems 14 (10): 711-728
    Appears in Collections:[資訊傳播學系] 期刊論文

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