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Aiming at the problem that the real-time performance of the system deteriorates due to the frequent calculation and matching descriptors of the feature-based visual odometry, a stereo visual odometry combined with optical flow tracking is proposed. Firstly, initialization is performed to generate initial key frames and map points, then in the tracking thread, use the optical flow tracking feature points to obtain the matching relationship, calculate and optimize the camera pose. After meeting the conditions for generating key frames, set the current frame as a key frame, extract the ORB feature points of the image, use descriptor matching to obtain the matching relationship with the feature points of the previous key frame, and triangulate to generate new map points. Secondly , the new keyframes and map points are optimized using the sliding window algorithm in the optimization thread,and redundant keyframes and map points are eliminated. In the KITTI data the experimental results show that the trajectory error of the proposed algorithm is at the same level as the ORB-SLAM3 algorithm in stereo mode, and the real-time performance is greatly improved.The average time for tracking each frame of image is reduced from 52ms to 16ms. In the case of ensuring high precision, the running speed is greatly improved, and it has high practical value.