| Summary: | 碩士 === 淡江大學 === 機械與機電工程學系碩士班 === 99 === In this thesis, a sparse and persistent map is established using the method of speeded-up robust features (SURF) and applied on the visual simultaneous localization and mapping (SLAM) based on the extended Kalman filter (EKF). Since SURF 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 image features from the image sequences. Therefore, it is suitable to be utilized as the map features in SLAM. The research topic of this thesis consists of three parts: first, the procedures of detection, description and matching of the SURF method are modified to improve the image processing speed and feature recognition rate. Second, the effective procedures of data association and map management for EKF SLAM are designed to improve the accuracy of robot state estimation. Finally, two cameras are employed as the only sensor of the SLAM system. The state prediction and estimation of features on image plane is performed using only the left camera. When new features are going to be added to the map, the image depth of these new features are calculated using a stereo vision formed by the left and right cameras. The EKF SLAM with SURF-based map is developed and implemented on a binocular vision system. The integrated system has successfully tested the basic capabilities of SLAM system, including ground truth and loop closure, as well as the ability of navigating over a long distance in indoor environments.
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