Open AccessAn improved ORB-SLAM2 algorithm based on image information entropy
Author(s) -
Xiaozhen Ren,
Yongye Wang,
Hongxiang Wang,
Xingzhen Li,
Xingxing Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012165
Subject(s) - artificial intelligence , entropy (arrow of time) , computer vision , orb (optics) , computer science , simultaneous localization and mapping , pattern recognition (psychology) , mobile robot , image (mathematics) , robot , physics , quantum mechanics
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.