Aiming at the shortcomings of traditional ORB algorithm which is sensitive to edges and has high mismatch rate

an improved ORB feature matching algorithm is proposed. Firstly

the improved FAST14—24 algorithm is used to extract the corner points

which overcomes the shortcomings of sonsitivity of the traditional ORB corner point detection algorithm to image edges. Then

in the corner point fine selection stage

the Shi-Tomasi corner point detection algorithm is used to eliminate most of the pseudo corner points

and the feature point set with higher repetition rate is obtained. Then use the improved retinal descriptor extraction algorithm to extract 512-dimensional binary descriptors

and train the descriptors to make the descriptors more distinguishable; Finally

the Hamming distance is used for matching in the feature matching phase. In the image matching experiment

the matching accuracy of the algorithm is improved by 18 %～ 62 % compared with the traditional ORB algorithm. The experimental results show that the proposed algorithm has higher matching precision and better robustness than the ORB algorithm.