Existing image matching algorithms in the field of visual navigation are primarily based on the similarity measure of descriptors. The large number of feature points required and the lack of consideration for the overall features of the image affect the real-time reliability of image matching. To that end, this paper proposes an overall matching algorithm for image feature points based on clustering analysis. The algorithm performs distance-based cluster analysis on the set of feature points to filter out representative feature points with a high repetition rate, divides the target image and the image to be matched into four regions based on the distribution of feature points, selects two feature points randomly from each region to calculate the basic matrix, performs overall feature point matching based on the epipolar constraint and position constraint, and checks the matching results based on the geometric similarity among the feature points. The images in Technical University of Munich RGB-Depth data set, unmanned aerial vehicles, and mobile robots are selected for the image matching test. The results show that the proposed algorithm has a high matching accuracy of 97.1% and an average matching time of less than 25 ms, which can meet the requirements of real-time matching.