An improved ORB-SLAM2 algorithm based on image information entropy. (December 2020)

• Record Type: Journal Article
• Authors: Ren, Xiaokang; Wang, Yongye; Wang, Hongxiang; Li, Xingzhen; Liu, Xingxing
• Is Part Of: Journal of physics. Volume 1693(2020)
• Publication Date: 2020-12
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1693/1/012165
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Aiming at the feature point extraction process in visual ORB-SLAM2, a visual SLAM method based on information entropy is proposed. In the process of ORB-SLAM2, due to the uncertainty of illumination and surrounding environment information, when the environment information is reduced, the number of feature points is likely to decrease dramatically, which results in the failure of SLAM process. In order to solve the above problems, a visual SLAM method based on image information entropy is proposed. According to the information entropy of the image, the amount of information is determined, and the image with low contrast and small gradient change is screened. The image is enhanced, and the feature points that can represent the image information are extracted as much as possible as the correlation basis of adjacent frame matching and key frame matching. In order to improve the performance of the mobile robot system, the local and global optimization of cumulative error of position and attitude is carried out by combining the optimization method of attitude graph.