A scalable framework for real-time, 6-DOF pose estimation of uncooperative space objects using monocular cameras, addressing dataset scarcity by generating mission-specific synthetic data augmented with style randomization to mitigate domain shift.
Abstract
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.
A novel framework for single-shot shape and pose estimation of unknown spacecraft objects using only a single image and reconstructed geometry is proposed, demonstrating strong generalization to unseen spacecraft.
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