Efficient approach for Binocular Vision-SLAM

摘要

This paper presents an approach to binocular vision simultaneous localization and mapping (SLAM). SIFT (Scale Invariant Feature Transform) algorithm is used to extract the Natural landmarks. But SIFT algorithm is complicated and computation time is long. Firstly, the linear combination of cityblock distance and chessboard distance is comparability measurement; secondly, partial features are used to matching. SLAM is completed by fusing the information of SIFT features and robot information with EKF. Mahalanobisis distance is used in data association which solve the problem that the scale of data association increase with the map grows in process of SLAM .The simulation experiment indicate that the proposed method reduce computational complexity, and with high localization precision in indoor environments.