Huang Yourui
Collage of Electrical and Information Engineering，Anhui University of Science and Technology，Huainan 232001,China；Wang Zhaofeng
Collage of Computer Science and Engineering，Anhui University of Science and Technology，Huainan 232001,ChinaHan Tao
Collage of Electrical and Information Engineering，Anhui University of Science and Technology，Huainan 232001,China；Song Hongping
Collage of Electrical and Information Engineering，Anhui University of Science and Technology，Huainan 232001,China；1.Collage of Electrical and Information Engineering，Anhui University of Science and Technology，Huainan 232001,China; 2.Collage of Computer Science and Engineering，Anhui University of Science and Technology，Huainan 232001,China
TP391.9；TN98
Aiming at the problem that visual SLAM is easily affected by moving objects such as vehicles and pedestrians in real environment, resulting in low pose estimation accuracy, a dynamic visual SLAM algorithm combined with lightweight YOLOv5s is proposed. The improved lightweight YOLOv5s is used as an object detection algorithm to judge moving objects. Combined with the proposed method of dynamic feature points elimination, dynamic feature points are eliminated, and only static feature points are used for pose estimation and map tracking. Experiments on TUM data set show that compared with ORB-SLAM3 algorithm, the pose estimation accuracy of the improved algorithm on high dynamic sequence is improved by 89.29%, 65.34% and 94.42% respectively. The results show that the improved algorithm can effectively eliminate dynamic feature points. The pose estimation and positioning accuracy of visual SLAM algorithm in dynamic environment are improved.
