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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/65478

    Title: Improving Data Association in Robot SLAM with Monocular Vision
    Authors: Wang, Yin-Tien;Hung, Duan-Yan;Sun, Chung-Hsun
    Contributors: 淡江大學機械與機電工程學系
    Keywords: data association;likelihood function;nearest-neighbor (NN) method;speeded up robust features (SURF);simultaneous localization and mapping (SLAM);monocular vision
    Date: 2011-11-01
    Issue Date: 2011-10-21 13:59:00 (UTC+8)
    Publisher: 臺北市:中央研究院資訊科學研究所
    Abstract: In the paper, an algorithm is proposed for improving the data association in robot visual Simultaneous Localization and Mapping (SLAM). The detection of speeded-up robust feature (SURF) is employed in the algorithm to provide a robust description for image features as well as a better representation of landmarks in the map of a visual SLAM system. Meanwhile, a likelihood-based tracking window and a nearest-neighbor (NN) method are utilized to match the high-dimensional data sets created for SURF. Experiments are carried out on a hand-held camera to verify the performances of the proposed algorithm for dealing with the data association problem in robot visual SLAM. The results show that the integration of the SURF features, the tracking window and the NN method is efficient in reducing the computational time and increasing the rate of successful feature matching.
    Relation: Journal of Information Science and Engineering 27(6), pp.1823-1837
    Appears in Collections:[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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