## 5 Conclusions  
This paper presents an enhanced visual dynamic object elimination SLAM algorithm, based on an improved YOLOv8, designed to reduce the impact of dynamic objects on localization and mapping accuracy in complex environments. The algorithm builds upon the ORB_SLAM3 framework by integrating a modified YOLOv8 with the LK optical flow method and introducing a dynamic feature point elimination algorithm. This approach efficiently detects and removes dynamic object feature points, minimizing their interference and thereby improving the SLAM system's accuracy. Key enhancements include replacing the YOLOv8 convolution module with the MSFConv module, which enhances both object detection precision and speed; utilizing the LK optical flow method to compute feature point optical flow vectors; and designing a dynamic feature point elimination strategy that combines optical flow vectors with object detection results. These modifications collectively enhance trajectory estimation accuracy in dynamic scenes. Experimental results demonstrate that the proposed method significantly outperforms ORB_SLAM3 and other state-of-the-art methods in high-dynamic scenarios, such as walking_half and walking_xyz sequences. Specifically, it achieves a reduction of 93.34% and 94.43% in the root mean square error (RMSE) of the absolute trajectory error (ATE), respectively. In low-dynamic scenes, such as sitting_static, the algorithm not only improves global accuracy but also achieves some local optimizations. MSF-SLAM's computational requirements (233.1 FPS on high-end hardware, 45.2 FPS on Jetson Xavier NX) make it viable for real-time robotics applications, including edge devices commonly used in autonomous systems. While ORB-SLAM3 is lighter, MSF-SLAM's superior accuracy in dynamic scenes justifies its resource demands for applications prioritizing robustness over minimal hardware constraints. This research has significant implications for robot localization and map construction in complex dynamic environments. Future work may focus on further optimizing feature extraction mechanisms to enhance performance in static scenes. The method excels in dynamic environments but faces challenges in near-static scenes (e.g., \"sitting_xyz\", ATE up 16.92%) due to over-removal of static features, struggles with poor lighting or occlusion due to YOLOv8 reliance, and incurs a computational cost (FPS drops from 275.1 to 233.1) that may limit use on low-power systems. In handling partially dynamic objects--such as a person sitting then standing--DOFF uses YOLOv8 anchor boxes and a 1.5-pixel optical flow threshold to distinguish static and moving features; however, subtle or intermittent motion can delay detection, potentially introducing errors in scenes like \"sitting_static\". Building on these findings, future work should enhance SLAM performance by tackling feature extraction and dynamic feature handling challenges, with directions including:  
* Exploring more advanced feature extraction modules that can further improve detection accuracy while reducing computational overhead. * Investigating adaptive dynamic point removal strategies to improve performance in low-dynamic or near-static environments. * Extending the framework to multi-sensor fusion scenarios to enhance SLAM robustness in real-world applications. Methodology, Y.D. ; Validation, J.L. ; Writing--original draft, Y.D. and J.L. ; Writing--review, and editing, Y.D. ; resources, X.Z. ; Supersion, X.Z. All authors have read and agreed to the published version of the manuscript. This research received no funding.