淡江大學機構典藏:Item 987654321/98331
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    題名: Improvement of Speeded-Up Robust Features for Robot Visual Simultaneous Localization and Mapping
    作者: Yin-Tien Wang;Guan-Yu Lin
    貢獻者: 機械與機電工程學系暨研究所
    關鍵詞: Robot mapping;Speeded-up robust features (SURF);Simultaneous localization and mapping (SLAM);Image processing;Robot vision
    日期: 2014-07-01
    上傳時間: 2014-07-18 11:41:17 (UTC+8)
    出版者: Cambridge University Press
    摘要: A robot mapping procedure using a modified speeded-up robust feature (SURF) is proposed for building persistent maps with visual landmarks in robot simultaneous localization and mapping (SLAM). SURFs are scale-invariant features that automatically recover the scale and orientation of image features in different scenes. However, the SURF method is not originally designed for applications in dynamic environments. The repeatability of the detected SURFs will be reduced owing to the dynamic effect. This study investigated and modified SURF algorithms to improve robustness in representing visual landmarks in robot SLAM systems. Many modifications of the SURF algorithms are proposed in this study including the orientation representation of features, the vector dimension of feature description, and the number of detected features in an image. The concept of sparse representation is also used to describe the environmental map and to reduce the computational complexity when using extended Kalman filter (EKF) for state estimation. Effective procedures of data association and map management for SURFs in SLAM are also designed to improve accuracy in robot state estimation. Experimental works were performed on an actual system with binocular vision sensors to validate the feasibility and effectiveness of the proposed algorithms. The experimental examples include the evaluation of state estimation using EKF SLAM and the implementation of indoor SLAM. In the experiments, the performance of the modified SURF algorithms was compared with the original SURF algorithms. The experimental results confirm that the modified SURF provides better repeatability and better robustness for representing the landmarks in visual SLAM systems.
    關聯: Robotica 32(4), pp.533-549.
    DOI: 10.1017/S0263574713000830
    顯示於類別:[機械與機電工程學系暨研究所] 期刊論文

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