In order to solve the problems of the ORB image feature detection algorithm under non-uniform illumination, such as overly clustered feature points and low accuracy of feature matching, we propose an efficient and high-precision illumination adaptive ORB image feature matching algorithm. The oFAST feature points of the image to be measured are extracted using the adaptive threshold, and the number of feature points in the low illumination or high exposure area is further increased through the uniform distribution of the optimized quadtree decomposition method. Then, feature matching is performed according to Hamming distance, and the improved RANSAC algorithm is used to eliminate mis-matching, so as to improve the matching accuracy of the feature points in the ORB algorithm. The experimental results show that for data sets with obvious illumination changes, compared with ORB, MA, Y-ORB and SORB algorithms, the average feature distribution uniformity of our proposed algorithm is improved by 13. 1%, the feature extraction time is saved by 26. 3%, and the comprehensive evaluation index is improved by 18. 5%. It can efficiently complete feature matching under complex environment changes, and has strong application value in the fields of target recognition and 3D reconstruction.