Vision-Based Relative Pose Estimation of Space Objects for Proximity Operations Using Lab-Validated Deep Learning
Abstract. This paper presents a scalable framework for real-time, 6-DOF pose estimation of uncooperative space objects using monocular cameras. We address dataset scarcity by generating mission-specific synthetic data augmented with style randomization to mitigate domain shift. A lightweight YOLOv11n-pose model extracts keypoints, which feed into a RANSAC-PnP solver for initial pose estimation, refined through an Extended Kalman Filter leveraging rigid body dynamics. Validation against synthetic sequences and laboratory experiments with a 1:1 replica demonstrates mean orientation errors of 3.9° and translation errors of 1.1%, matching state-of-the-art performance. The pipeline maintains robustness under challenging lighting conditions and domain shift, achieving ~50 fps on GPU and ~5 fps on CPU, enabling deployment on resource-constrained platforms for on-orbit servicing and debris removal missions.