RT-SLAM: A Real-Time Visual SLAM System Integrating Enhanced RT-DETR and Optical Flow Techniques

To enhance the reliability and stability of simultaneous localization and mapping (SLAM) in dynamic environments, we propose a novel SLAM system integrating an advanced real-time object detection algorithm, the real-time detection transformer (RT-DETR). Our approach combines RT-DETR’s object detection capabilities with an optical flow-based dynamic thresholding method, effectively filtering out feature points associated with dynamic objects and thereby improving SLAM performance in such environments. We have optimized RT-DETR by substituting its original network backbone with lightweight modules, which reduces the number of parameters by 45% while only incurring a 5% reduction in accuracy. This optimization significantly lowers computational costs, making it feasible for deployment on mobile devices. Experiments conducted on the TUM and BONN dynamic datasets demonstrate that our system reduces the root mean square error (RMSE) of absolute trajectory and relative pose error (RPE) by approximately 28.82% compared to oriented fast and rotated brief-SLAM3 (ORB-SLAM3). Furthermore, experiments conducted on both a high-performance device and an embedded device demonstrate that, compared to Crowd-SLAM, which employs you only look once (YOLO) for dynamic object removal, our approach achieves an 8.52% improvement in absolute trajectory error (ATE), while the average frame per second (FPS) only decreases by 3.07%.