Aiming at the problem of low stability and low solution accuracy when using the RANSAC algorithm to solve the essential matrix in the large-scale measurement scene of the monocular system，an improved RANSAC method for solving the essential matrix is proposed．The essential matrix obtained from the points is used to reproject the remaining matching feature points，and use the relative discriminant method to determine whether the current inlier is a high-quality inlier through the value of these errors，and then use the dichotomy method to dynamically adjust the threshold on this basis to find the optimal value from several essential matrices．Finally，this paper designs RANSAC experiments under different mis-matching rates of multiple perspectives．The experiments prove that，compared with traditional and other improved RANSAC algorithms and LMedS algorithms，The improved algorithm in this paper can quickly determine the initial interior points and adaptively adjust the threshold，and at the same time obtain a better essential matrix，which meets the requirements of solution stability and accuracy．