Abstract: The feature point matching can generate a corresponding relationship between the input images. It is a basic and important module in visual odometry and has a wide application in different computer vision fields. Random sample consensus (RANSAC) is a widely used image matching algorithm, but it has the disadvantages of low recall rate and long time-consuming. By considering the grid motion statistics method and the sequence selection strategy, this paper proposes an improved RANSAC algorithm. Firstly, the quality of the initial feature matching is sorted, based which the input image is divided into a certain number of grids and the matching statistics in the grid is performed according to the motion smoothness theory. Then the grids with higher scores are selected to estimate the local homography matrix, respectively. Moreover, the local homography matrices are aggregated to further eliminate the influence of noise and obtain the optimal model. In addition, the sequential selection strategy is used to obtain the homography matrix, which further improves the efficiency of the proposed algorithm. Finally, the simulation results show that the proposed image matching algorithm based on grid acceleration and sequential selection strategy has better performance.