Binocular Vision-SLAM Using Improved SIFT Algorithm

Abstract

SIFT (Scale Invariant Feature Transform) algorithm is used in mobile robot Simultaneous Localization and Mapping (SLAM) based on visual information. But this algorithm is complicated and computation time is long. Two improvements are introduced to optimize its performance. 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. The simulation experiment indicate that the proposed method reduce computational complexity, and with high localization precision in indoor environments.