Objective To propose a scoring framework grid-based motion statistics (SF-GMS) feature matching algorithm to improve the poor real-time ability and inaccurate matching in the process of target recognition for medical service robots.Methods The feature point neighborhoods were segmented by SF-GMS algorithm using the grids, and the number of feature points in each neighborhood was counted and the scoring frame function was set to judge the feature matching accuracy according to the number of neighborhood feature points and the scoring threshold.Results and conclusion Compared with random sample consensus algorithm, SF-GMS algorithm effectively improved the successful matching rate, and had better real-time performance. SF-GMS algorithm had better stability to the changes of illumination view, occlusion, affine, scale and rotation, and could meet the demand of autonomous navigation in simulating hospital ward scenario for medical service robots.