Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features

淡江大學機構典藏 > 工學院 > 機械與機電工程學系暨研究所 > 會議論文 > Item 987654321/79060

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题名:	Robot Simultaneous Localization and Mapping Using Speeded-Up Robust Features
作者:	Wang, Yin-tien;Chi, Chen-tung;Feng, Ying-chieh
贡献者:	淡江大學機械與機電工程學系
关键词:	Robot Mapping;Speeded-Up Robust Features (SURF);EKF-SLAM
日期:	2012-11-02
上传时间:	2012-11-25 14:14:59 (UTC+8)
摘要:	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.
显示于类别:	[機械與機電工程學系暨研究所] 會議論文

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