  Feature matching is a fundamental and crucial process in visual SLAM, and
precision has always been a challenging issue in feature matching. In this
paper, based on a multi-level fine matching strategy, we propose a new feature
matching method called KTGP-ORB. This method utilizes the similarity of local
appearance in the Hamming space generated by feature descriptors to establish
initial correspondences. It combines the constraint of local image motion
smoothness, uses the GMS algorithm to enhance the accuracy of initial matches,
and finally employs the PROSAC algorithm to optimize matches, achieving precise
matching based on global grayscale information in Euclidean space. Experimental
results demonstrate that the KTGP-ORB method reduces the error by an average of
29.92% compared to the ORB algorithm in complex scenes with illumination
variations and blur.
