Optimization method to improve visual SLAM in dynamic environment

Aiming at the problem that the robustness of the classic visual SLAM system is greatly affected by dynamic target feature points in the environment, a method is proposed to use a target detection algorithm to identify and eliminate dynamic target feature points.First, use the target detection algorithm YOLOv5 to identify the collected environmental images, and select the surrounding environment.Objects identified as dynamic targets in the environment, and then the target detection results are integrated into the feature extraction of the visual SLAM frontend, the feature points belonging to the dynamic target part of the extracted image feature points are removed, and the remaining static feature points are used to map Construction and positioning, and finally testing on the TUM data set.The results show that after using the target detection algorithm to eliminate dynamic feature points, the root mean square error of the absolute trajectory error of the visual SLAM system in highly dynamic scenes is reduced by 97.89%, effectively improving the positioning accuracy and robustness of the system.