關鍵字查詢 | 類別:期刊論文 | | 關鍵字:Improving Data Association in Robot SLAM with Monocular Vision

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序號 學年期 教師動態
1 100/1 機電系 孫崇訓 副教授 期刊論文 發佈 Improving Data Association in Robot SLAM with Monocular Vision , [100-1] :Improving Data Association in Robot SLAM with Monocular Vision期刊論文Improving Data Association in Robot SLAM with Monocular VisionWang, Yin-Tien; Hung, Duan-Yan; Sun, Chung-Hsun淡江大學機械與機電工程學系data association;likelihood function;nearest-neighbor (NN) method;speeded up robust features (SURF);simultaneous localization and mapping (SLAM);monocular vision臺北市:中央研究院資訊科學研究所Journal of Information Science and Engineering 27(6), pp.1823-1837In 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 per
2 100/1 機電系 王銀添 教授 期刊論文 發佈 Improving Data Association in Robot SLAM with Monocular Vision , [100-1] :Improving Data Association in Robot SLAM with Monocular Vision期刊論文Improving Data Association in Robot SLAM with Monocular VisionWang, Yin-Tien; Hung, Duan-Yan; Sun, Chung-Hsun淡江大學機械與機電工程學系data association;likelihood function;nearest-neighbor (NN) method;speeded up robust features (SURF);simultaneous localization and mapping (SLAM);monocular vision臺北市:中央研究院資訊科學研究所Journal of Information Science and Engineering 27(6), pp.1823-1837In 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 per
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