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This study presents an enhanced visual SLAM (Simultaneous Localization and Mapping) framework that integrates ORB-SLAM3 with the YOLOv5 real-time object detection model to improve pose accuracy in dynamic environments. Although ORB-SLAM3 achieves robust performance in static scenes, its reliance on ORB feature tracking often degrades accuracy in the presence of moving objects. To overcome this limitation, YOLOv5 is employed to identify dynamic regions in each video frame, enabling the system to remove motion-related feature points before matching. This filtering mechanism reduces the influence of dynamic objects on trajectory estimation and enhances overall system robustness. The proposed method was evaluated using dynamic datasets, including BONN and TUM RGB-D, and further validated through real-world experiments with an Intel RealSense D435i camera. Experimental results demonstrate substantial improvements in pose accuracy compared with the baseline ORB-SLAM3 and the RTAB-Map system, confirming the effectiveness of the YOLOv5-assisted ORB-SLAM3 integration in dynamic scenes.
Ali et al. (Sat,) studied this question.