Aiming at the problems of low matching accuracy and poor real-time performance of traditional local feature matching algorithms in complex scenes，an improved image matching algorithm based on descriptor fusion and AGAST algorithm is proposed.Firstly，the template image and the image to be matched are filtered by Gaussian filter.Secondly，the grid AGAST algorithm based on adaptive threshold is used to extract feature points，which makes the distribution of feature points more uniform.Thirdly，the BRIEF descriptor and BEBLID descriptor are fused for the first time，and a new descriptor named BRBLID is proposed. The FLANN algorithm is then used to perform a preliminary matching of the descriptors.Finally，the RANSAC algorithm is applied to eliminate the feature point pairs with large matching errors.Experimental results show that the improved algorithm performs well in image matching in complex scenes，and the matching accuracy and efficiency are greatly improved with higher reliability and robustness