Optimization Algorithm of Visual Odometry for SLAM Based on Local Image Entropy

For the problems of failed matching and tracking loss due to big camera rotation in simultaneous localization and mapping (SLAM) for mobile robots, an optimized detail enhancement algorithm of visual odometry based on the local image entropy is proposed. The image pyramid is built and is divided into blocks on each level to extract features homogeneously. The information of each image block is determined through its entropy value and the blocks with low contrast and small intensity gradient will be deleted to reduce feature calculation. Nonlinear and adaptive illumination adjustment on each reserved block is applied to increase local image details. Local features that representing image information is preserved as much as possible to be the correlations between adjacent frames and keyframes. Combined with the pose graph optimization method, the local and global optimization of accumulation error is carried out to further improve the system performance for mobile robot. The proposed method is verified on the TUM dataset. Since using the feature properties which are more reflective of texture and shape, the maximum success rate of motion tracking is increased to over 60 %. And the results also show that the tracking error, translational error and rotation error is decreased. Compared with the original system ORB-SLAM2, this method can not only improve visual positioning accuracy of the mobile robot, but also meet the application need of real-time SLAM requirement. Copyright © 2021 Acta Automatica Sinica. All rights reserved.