SLAM Based on Deep Learning and Optical Flow Constraints in Dynamic Scenes
To address the problems of low localization accuracy and map vignetting in Visual Simultaneous Localization and Mapping(VSLAM)technology in dynamic environments,this paper proposes a dynamic SLAM algorithm based on deep learning.The proposed algorithm utilizes YOLOv8n,which has few network parameters and a high target recognition rate,to improve the visual front end of the system,add semantic information to the visual front end,and extract the dynamic region feature points.The LK optical flow method is then used to identify the dynamic feature points in the dynamic region,eliminate these dynamic feature points,and retain the static feature points in the dynamic region so as to improve the utilization rate of feature points.In addition,the proposed algorithm increases the map construction thread,eliminates the dynamic object point cloud extracted by YOLOv8n,receives the semantic information extracted by the front end,constructs a static semantic map,and eliminates the virtual shadow produced by dynamic objects.Experimental verification indicates that the proposed algorithm improves the localization accuracy in dynamic environments by 92.71％as compared to that of ORB-SLAM3.Further,it achieves a small improvement compared with other dynamic vision SLAM algorithms.
deep learningdynamic SLAMYOLOv8nstatic semantic mapoptical flow
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