Research on SLAM application with improved ORB extraction and matching algorithms
As the traditional ORB feature point extraction and matching method is not rich in image texture information or when the lighting changes drastically,it is very easy to produce feature point loss,uneven distribution and other problems,which is not conducive to the location and construction of the SLAM system.In this paper,a set of more robust and higher accuracy extraction matching algorithm is proposed.Firstly,the extraction algorithm is improved based on the ORB feature points,the adaptive threshold is calculated and the feature points are extracted based on the grid model,which can improve the robustness of feature point extraction and make its distribution uniform.In addition,the G-R image matching algorithm is also proposed,which calculates the neighborhood support estimator based on grid features to distinguish between positive and incorrect matches,and then combines with the RANSAC algorithm that introduces the evaluation function to further eliminate incorrect matches,which improves the matching accuracy by 9.36%compared with the original matching algorithm of ORB-SLAM2,and reduces the time consumption by about 13.6%.Finally,the feature point extraction matching algorithm proposed in this paper is added to the ORB-SLAM2 algorithm framework,which is verified by the dataset and the actual scene that the method in this paper can effectively improve the positioning accuracy of the ORB-SLAM2 system by more than 36.6%and make the system more robust.
digital image processingORB feature pointsvisual SLAMquadtreeGMS matching
万方数据
维普
