Binocular Ranging Method Based on Improved ORB-RANSAC

Aiming at the problems of high mismatch rate and low measurement accuracy of the traditional binocular vision measurement method based on feature point matching, a binocular ranging method based on ORB (Oriented Fast and Rotated Brief) feature and random sample consensus (RANSAC) is proposed in this paper. First, the method of combining epipolar constraint based on binocular position information and feature matching based on Hamming distance is used to delete mismatched points, get the correct matching point pair initially screened. Then, the sequential consistency constraint method of nearest neighbors based on k-dimension tree is used to screen out the initial interior point set, and the iterative pre-check method is used to improve the matching speed of RANSAC. Finally, in order to improve measurement accuracy, the sub-pixel point disparity is obtained by quadric surface fitting, and calculated actual distance. Experiments show that the method can effectively improve the matching speed and measurement accuracy of features, and meet the requirements of real-time measurement.