CycleGAN Enhanced Computer Vision Pipeline for Spacecraft Pose Estimation

The rapid expansion of space activities has created unprecedented orbital congestion, with over 11,700 active spacecraft demanding sophisticated autonomous operations and enhanced Space Situational Awareness capabilities. This thesis addresses the challenge of accurate six-dimensional spacecraft pose estimation —essential for autonomous docking and proximity operations— through a computer vision pipeline enhanced by domain adaptation techniques. The methodology integrates YOLO11n for object detection, EfficientNet-B0 for keypoint regression, and Perspective-n-Point algorithms for pose estimation, creating a computationally efficient framework suitable for onboard deployment. The synthetic-to-real domain gap is addressed using Cycle-Consistent Generative Adversarial Networks (CycleGAN) for unsupervised domain adaptation through adversarial image-to-image translation. Experimental validation using the SPEED+ dataset revealed severe performance degradation when transferring from synthetic to real domains, with keypoint localization accuracy deteriorating by 13-14 fold and pose estimation orientation error increasing by 19-22 fold. The proposed CycleGAN-based framework achieved substantial improvements: 67% reduction in pose estimation error for lightbox conditions, improving orientation accuracy from 34.13° to 11.37°. Sunlamp domain results showed mixed performance, indicating operational limits under extreme illumination conditions. Ablation studies identified keypoint regression as the primary performance bottleneck, contributing 52% additional improvement beyond object detection adaptation alone. The research establishes that cross-domain generalization is achievable through unsupervised domain adaptation using only unpaired imagery, providing a viable pathway for deploying synthetic-trained models in real space scenarios without extensive annotation campaigns, advancing autonomous spacecraft operations in increasingly congested orbital environments.