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A stereo visual odometry based on SURF feature and three consecutive frames

Visual Odometry is the process of estimating 6 DOF motion of a vehicle equipped with a single or multiple cameras. This technique has many potential applications, such as autonomous navigation and driving assistant in intelligent transportation field, which is an important part of smart city. In this paper, the basic framework of stereo visual odometry is reviewed, and a new 3D position estimation method in three consecutive frames is proposed. The main contributions of this paper include: (1) SURF of stereo image sequences is used in the process of detecting and matching features; (2) by saving historical matching result, feature matching is performed on three consecutive frames instead of just two frames without additional computation; (3) in the 3D-to-2D motion estimating step, 3D position is estimated from three consecutive frames instead of just two frames, thereby we can obtain more accurate results. We apply our method on the KITTI datasets, and the results show an accurate trajectory estimation over several hundred meters.