A Unified Framework for Cross-Domain Space Drone Pose Estimation Integrating Offline Domain Generalization with Online Domain Adaptation
Year: 2025
Type: article
Abstract: In this paper, we present a Unified Framework for cross-domain Space drone Pose Estimation (UF-SPE), addressing the simulation-to-reality gap that limits the deployment of deep learning models in real space missions. The proposed UF-SPE framework integrates offline domain generalization with online unsupervised domain adaptation. During offline training, the model relies exclusively on synthetic images. It employs advanced augmentation techniques and a multi-task architecture equipped with Domain Shifting Uncertainty modules to improve the learning of domain-invariant features. In the online phase, normalization layers are fine-tuned using unlabeled real-world imagery via entropy minimization, allowing for the system to adapt to target domain distributions without manual labels. Experiments on the SPEED+ benchmark demonstrate that the UF-SPE achieves competitive accuracy with just 12.9 M parameters, outperforming the comparable lightweight baseline method by 37.5% in pose estimation accuracy. The results validate the framework’s efficacy and efficiency for robust cross-domain space drone pose estimation, indicating promise for applications such as on-orbit servicing, debris removal, and autonomous rendezvous.
Source: Drones
Authors: Yingjian Yu, Zhang Li, Qifeng Yu
Institution: National University of Defense Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G7616492862
OpenAlex ID: W4416003826