To solve the problem of low efficiency of random sample consensus (RANSAC) algorithm in fast removing error matching, a sequential sample consensus (PROSAC) algorithm is proposed to improve the error matching deletion method in orb-slam2 framework. By using the matching quality of feature points to pre sort the feature points, the number of iterations in the process of image matching is reduced. A global BA subsection optimization algorithm based on maximum cut Ncut algorithm is proposed to reduce the computational complexity. Through the data set verification, the results show that the optimized simultaneous localization and mapping (SLAM) system improves the efficiency of error matching removal of the same image by 50% under the condition that the absolute trajectory and relative pose errors are basically consistent with ORB-SLAM2, which proves the effectiveness of the algorithm.