An algorithm for binocular vision measurements based on local information entropy and gradient drift is proposed to solve the low detection efficiency, low matching accuracy, and insufficient binocular vision measurement accuracy of traditional feature detection algorithms. First, the image is divided into several sub-regions, the local information entropy of each sub-region is calculated to screen out the high-entropy regions, and the oriented FAST and rotated BRIEF (ORB) algorithm is used to detect feature points. Second, the circular neighborhood is used to replace the pixel points, and the gradient amplitude of each pixel in the circular neighborhood is improved using two-dimensional Gaussian weighting to improve the rotation invariant local binary patterns (LBP). Next, it is fused with the rotated binary robust independent elemental features (rBRIEF) to generate a new descriptor for feature matching. Finally, the gradient drift method is proposed. The sub-maximum response value of the feature point is introduced as the auxiliary factor. Combined with the maximum response value, the accurate coordinates of the ideal feature point are calculated through the iterative coordinate update, solving the inaccurate feature point coordinates and improving the measurement accuracy. The experimental results show that the average matching accuracy of the proposed algorithm is 37.51% higher than that of the traditional ORB algorithm, and the lowest relative measurement error is 0.365%.