Visual Localization Algorithm with Dynamic Point Removal Based on Multi-Modal Information Association

To enhance the autonomous navigation capability of intelligent agents in complex environments, this paper presents a visual localization algorithm for dynamic scenes that leverages multi-source information fusion. The proposed approach is built upon an odometry framework integrating LiDAR, camera, and IMU data, and incorporates the YOLOv8 model to extract semantic information from images, which is then fused with laser point cloud data. We design a dynamic point removal method based on multi-modal association, which links 2D image masks to 3D point cloud regions, applies Euclidean clustering to differentiate static and dynamic points, and subsequently employs PnP-RANSAC to eliminate any remaining undetected dynamic points. This process yields a robust localization algorithm for dynamic environments. Experimental results on datasets featuring dynamic objects and a custom-built hardware platform demonstrate that the proposed dynamic point removal method significantly improves both the robustness and accuracy of the visual localization system. These findings confirm the feasibility and effectiveness of our system, showcasing its capabilities in precise positioning and autonomous navigation in complex environments.