In this paper, regarding the phenomenon that the basic feature point matching algorithm is prone to mismatch in visual odometry, we proposes a ring matching algorithm of feature points combined with the bidirectional optical flow method. This algorithm forms a ring structure between the stereo image and the images in the front and rear frames. For the images in the front and rear frames, the bidirectional pyramid optical flow method is used to track feature points and eliminate mismatched feature points. The basic feature point matching algorithm usually adopts fast library for approximate nearest neighbors (FLANN), but the result contains many mismatched point pairs. The proposed matching algorithm can not only eliminate the mismatched feature points but also make the feature points evenly distributed on the images. Subsequently, the perspective-3-point (P3P) algorithm based on 3D-2D points is combined with random sample consensus (RANSAC) to obtain the initial pose estimation results. The general graph optimization (g2o) library is employed to further optimize the pose estimation results. The positioning experiments verify that the ring matching algorithm of feature points combined with the bidirectional optical flow method has higher positioning accuracy.