Binocular Vision SLAM Algorithm Based on Dynamic Region Eliminationin Dynamic Environment

In dynamic environments, the localization and mapping accuracy of the existing simultaneous localization and mapping (SLAM) algorithms will decrease dramatically. For this problem, a binocular vision SLAM algorithm based on dynamic region elimination is proposed. Firstly, the dynamic sparse feature points in the scene are identified by the stereo vision based geometric constraint method, and the scene area is segmented based on the scene depth and color information. Secondly, the dynamic points and the scene segmentation results are used to mark the dynamic regions in the scene, and then eliminate the feature points in the dynamic regions in the existing binocular ORB-SLAM algorithms as well as the impact of dynamic targets in the scene on SLAM accuracy. Finally, the experimental verification of the proposed algorithm is carried out. The recall rate of dynamic region segmentation on the KITTI dataset can reach 92.31%, and it can reach 93.62% in the test of visual guidance in the outdoor environment. Compared with the previous binocular ORB-SLAM algorithm, the straight walking localization accuracy is improved by 82.75%, and the mapping effect is also enhanced significantly. The average processing rate of the algorithm can reach 4.6 frame/s. The results show that the proposed algorithm can significantly improve the localization and mapping accuracy of the binocular vision SLAM algorithm in dynamic scenes, and meets the real-time performance requirement of visual guidance.