A method of dense point cloud SLAM based on improved YOLOV8 and fused with ORB-SLAM3 to cope with dynamic environments

Abstract

With the development of society and the advancement of technology, intelligent robots have been widely used in various fields. At the same time, Simultaneous Localization and Mapping (SLAM) technology is a key technology in the research field of intelligent robots. However, in dynamic environments, achieving accurate and robust visual SLAM remains a major challenge. In this paper, we propose a method based on improved YOLOv8 fused with ORB-SLAM3 to address dense point cloud SLAM in dynamic environments. Our proposed method successfully integrates real-time object detection and image segmentation technologies of YOLOv8 into the ORB-SLAM3 framework, achieving high-precision and robust visual SLAM in dynamic environments. In the YOLOv8 framework, we use a balanced convolution method, GSConv, instead of some traditional convolution layers (Conv), which balances accuracy with computational load. Based on the GSConv convolution method, we adopt a new feature fusion module, VoVGSCSP, to replace traditional C2f feature fusion modules, thereby improving the Neck structure of YOLOv8 and achieving a lightweight network model. We compare our proposed method with ORB-SLAM3 and some computer vision algorithms on the TUM dataset. Experimental data confirms that our method outperforms existing visual SLAM algorithms in dynamic environments. In fast-moving dynamic environments, the RMSE of absolute pose estimation of our method is 96.28% lower than that of ORB-SLAM3, and the RMSE of relative pose estimation is 51.57% lower than that of ORB-SLAM3. The experimental results demonstrate that our method significantly improves the accuracy of pose estimation in dynamic environments and greatly enhances the performance compared to ORB-SLAM3.