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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92585


    Title: Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features
    Authors: Wang, Yin-tien;Chi, Chen-tung;Feng, Ying-chieh
    Contributors: 淡江大學機械與機電工程學系
    Keywords: Robot Mapping;Speeded-Up Robust Features (SURF);EKF-SLAM
    Date: 2013-01-01
    Issue Date: 2013-10-19 12:09:06 (UTC+8)
    Publisher: Stafa-Zurich: Trans Tech Publications Ltd.
    Abstract: An algorithm for robot mapping is proposed in this paper using the method of speeded-up robust features (SURF). Since SURFs are scale- and orientation-invariant features, they have higher repeatability than that of the features obtained by other detection methods. Even in the cases of using moving camera, the SURF method can robustly extract the features from image sequences. Therefore, SURFs are suitable to be utilized as the map features in visual simultaneous localization and mapping (SLAM). In this article, the procedures of detection and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. The sparse representation of SURF is also utilized to describe the environmental map in SLAM tasks. The purpose is to reduce the computation complexity in state estimation using extended Kalman filter (EKF). The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has been successfully validated to fulfill the basic capabilities of SLAM system.
    Relation: Applied Mechanics and Materials 284-287, pp.2142-2146
    DOI: 10.4028/www.scientific.net/AMM.284-287.2142
    Appears in Collections:[機械與機電工程學系暨研究所] 期刊論文

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