R$^{2}$D-SLAM: A Real-Time and Robust Visual SLAM Framework for Autonomous Navigation in Dynamic Environments

Classical Simultaneous Localization and Mapping (SLAM) algorithms, fundamental to autonomous navigation, often assume a static environment, an assumption that fails in many real-world scenarios, resulting in significant trajectory drift. This creates a long-standing trade-off in dynamic SLAM: high-accuracy methods are typically too slow for real-time deployment, while real-time systems often suffer from reduced robustness. To address this challenge, we propose R2D-SLAM, a novel real-time visual SLAM framework that achieves both high accuracy and efficiency in dynamic environments. Our system integrates a lightweight and effective dynamic object detector, Detection Transformer (DETR), with an enhanced matching strategy, termed DEIM_Dyna, into the robust ORB-SLAM3 framework. A dedicated parallel thread performs dynamic region detection concurrently with the main SLAM pipeline, enabling early rejection of contaminated features during tracking. This ensures that pose estimation is conducted on a purified, static feature set, significantly improving robustness and accuracy. Extensive experiments on the challenging TUM and BONN dynamic datasets show that R2D-SLAM achieves new state-of-the-art performance in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), while maintaining real-time processing speeds exceeding 25 FPS. R2D-SLAM effectively bridges the gap between accuracy and efficiency, offering a practical and robust solution for dynamic scene perception in autonomous vehicles and Unmanned Aerial Vehicles (UAVs).