Registration of sequential star images is an important component of space observation. Especially for onboard devices, the efficiency and robustness of registration algorithms are particularly important. The typical star image registration approach based on star matching has a low tolerance for incorrect matching, and the time cost will increase rapidly as the number of stars increases. Due to the high overlap and rigid transformation of adjacent star images, a proposed method based on star angular distance (SAD) is presented. Stars are easy to locate and extract as natural feature points, and there are a large number of identical stars in adjacent star images. The rotation and translation of the SAD, composed of identical stars in adjacent star images, are the same. Therefore, maximum intersection clustering (MIC) was proposed to cluster rotation and translation, and Gaussian weight iteration (GWI) was proposed to estimate rigid transformation parameters. The use of SAD as a star image feature reduces the complexity of star image features, which can improve the efficiency of the algorithm. MIC can tolerate errors within a certain range, and GWI can lessen their impact on the results, increasing the algorithm's robustness. Experimental results show that the proposed method can improve the trend of rapidly increasing computation as the number of stars increases and avoid the restriction that transformation parameters must be obtained with correctly matching stars. Compared to the typical triangle method and SAD similarity method, the proposed method has higher efficiency under different numbers of stars, and translation, rotation, and location errors.