Abstract: RGB-D SLAM is a technology that utilizes depth cameras to achieve simultaneous localization and mapping (SLAM). Traditional visual SLAM systems are based on the assumption of a static environment, yet dynamic objects often exist in real-world scenarios, potentially leading to significant deviations in the pose estimation of SLAM systems. To address this issue, this paper proposes a SLAM system based on lightweight YOLOv8s object detection. This system employs Socket communication to transmit object detection results to the SLAM system, which then utilizes the Depth Value-RANSAC geometric algorithm to eliminate dynamic feature points within the detected bounding boxes, thereby enhancing the positioning accuracy of the SLAM system in dynamic environments. The experiments were conducted using the TUM dataset for validation, and the results indicate that the systems accuracy is significantly improved compared to ORB-SLAM2. Compared to other SLAM systems, varying degrees of improvement in accuracy and real-time performance were observed.

Key words: RGB-D SLAM, dynamic scene, object detection, geometric constraint