Abstract: In order to solve the problem of insufficient positioning accuracy, error accumulation and long time consuming of feature point matching method, a visual odometer that combines RANSAC optical flow method and improved feature point matching method was designed. The RANSAC optical flow method was used to estimate the small-scale motion between key frames. The RANSAC algorithm eliminated the mismatch points of optical flow, which greatly reduced the mismatch existing in the optical flow method. The motion estimation between key frames used the improved feature point matching method to correct the estimation error of the optical flow method. Finally, the RANSAC optical flow method and the improved feature point matching method were fused by using the Kalman filtering. The experimental results show that the algorithm can overcome the problems of insufficient accuracy and error accumulation of the optical flow method, which increases the MRE from 15.5% to 2.6%. And it can also improve the speed of the feature point matching method, which increases the average consuming time from 37.28 ms to 21.07 ms.