DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects

-- We present DynamicPose, a retraining-free 6D pose tracking framework that improves tracking robustness in fast-moving camera and object scenarios. Previous work is mainly applicable to static or quasi-static scenes, and its performance significantly deteriorates when both the object and the camera move rapidly. T o overcome these challenges, we propose three synergistic components: (1) A visual-inertial odometry compensates for the shift in the Region of Interest (ROI) caused by camera motion; (2) A depth-informed 2D tracker corrects ROI deviations caused by large object translation; (3) A VIO-guided Kalman filter predicts object rotation, generates multiple candidate poses, and then obtains the final pose by hierarchical refinement. The 6D pose tracking results guide subsequent 2D tracking and Kalman filter updates, forming a closed-loop system that ensures accurate pose initialization and precise pose tracking. Simulation and real-world experiments demonstrate the effectiveness of our method, achieving real-time and robust 6D pose tracking for fast-moving cameras and objects. I. INTRODUCTION Recent advances in CAD model-based pose estimation and tracking [1]-[4] have significantly improved object generalization capabilities, enabling robotic systems to interact with diverse objects without retraining. Of particular importance for mobile robotic applications, robust 6D pose tracking serves as the cornerstone for enabling critical manipulation tasks, including autonomous grasping and physical scene interaction on unmanned aerial vehicles (UA Vs) and autonomous ground vehicles (AGVs), etc. While existing 6D pose tracking methods demonstrate robust performance under gradual motion conditions, their accuracy deteriorates catastrophically in fast-moving scenarios characterized by rapid camera or object movements.