ing at the problems that there exist poor accuracy of the optical flow method and time consumption of the feature point method in traditional visual odometers, we propose the model of a visual odometer by integrating optical flow with feature matching. This model mainly fuses the LK optical flow pose estimation based on inter-frame optimization with the optical flow / feature point pose optimization based on key frames. In addition, aiming at the problem that there occur accumulation errors in the traditional reference-frame/current-frame tracking method, we introduce a local optimization algorithm on the basis of the optical flow method to preliminarily estimate the camera's pose. Simultaneously, aiming at the problems that the image insertion frequency is too high and time consumption in the feature method, we construct a unified loss function of optical flow/feature points on the basis of the key frames to optimize the camera’s pose. The position accuracy test results of the algorithm on the EuRoC dataset show that the position accuracy of the proposed algorithm in simple environments is equivalent to that of the feature point method, and in the case of missing feature points, the proposed algorithm possesses position accuracy higher than that of the feature point method and has certain robustness. The running time test results show that on the basis of ensuring the positioning accuracy, the running time of the proposed algorithm is 37.9% less than that of the feature point method, and the algorithm has the certain real-time performance.