Unmarked satellite pose estimation with semi-supervised domain adaptation and information augmentation

Abstract

Research on computer vision-based unmarked satellite pose estimation is gaining increasing attention in fields such as intelligent control, industrial communication, and automation technology. A novel unmarked satellite pose estimation method based on semi-supervised domain adaptation and information augmentation is proposed. Efficient pseudo- label generation is achieved by utilizing the homography properties of satellites. Additionally, data enhancement is performed using limited target datasets, sparse satellite feature information, and image preprocessing. The augmented target dataset undergoes filtering and semi-supervised training through iterative merging of source and target datasets until optimization of the loss function related to pixel differences is accomplished. Finally, geometric constraint equations are constructed using the 2D keypoints and the 3D skeleton model of the satellite, with these equations being solved to obtain the satellite’s pose. Extensive experiments are conducted on common satellite pose estimation datasets, and the effectiveness of the proposed method is demonstrated.