ORB feature extraction and feature matching based on geometric constraints

This paper studies feature extraction and feature matching in visual odometry. Aiming at the problems that ORB feature extraction does not have illumination invariance and feature distribution is uneven, an adaptive threshold algorithm for feature extraction is added, and a quadtree is used to manage feature points. Aiming at the problem of high time cost of the feature matching algorithm, an outlier removal algorithm based on geometric constraints is proposed, and the constraint set is constructed by using the slope, distance, and descriptor distance between the matching feature point pairs. Tested on the TUM dataset, the feature extraction algorithm can adapt to scenes with different brightness, and the robustness is improved. The time taken by outlier removal algorithm based on geometric constraints is about 10% of RANSAC. After that, combined with RANSAC, the running time of RANSAC can be reduced by 60%. Our algorithm can improve the estimation accuracy and robustness of the system.