Image matching algorithm in outdoor environment

Abstract

In order to solve the problem that the traditional feature matching algorithm has less premise number of feature points and poor matching ability under outdoor complex lighting conditions, an image matching algorithm based on color invariants in outdoor environment is proposed. Firstly, a feature matching algorithm with color invariants and Tanimoto similarity is designed based on Kubelka Munk theory. By introducing color invariants to distinguish the available feature areas in outdoor scenes, AKAZE (Accelerated KAZE) algorithm and SIFT (Scale invariant Feature Transform) algorithm are combined to generate more comprehensive feature descriptors; Then, Tanimoto similarity test is used to screen feature point pairs and random sample consensus algorithm is used to remove external points. According to the experimental results, under the same conditions, the improved algorithm obtains more effective feature points at the edge of the image and in the smooth area of the image. The average accuracy of the algorithm in outdoor environments reaches 90%, and the number of feature matching is 43% higher than that without color invariants.