Deep-Learning-Based Direct Attitude Estimation for Uncooperative Known Space Objects

Attitude estimation for uncooperative known space objects plays a critical role in intelligent space technology. In recent years, deep-learning-based methods for estimating the attitude of space objects have made significant progress, particularly through indirect methods based on key point detection. While highly accurate, the indirect method requires high texture detail and is time-consuming, which limits its application in convolutional neural network (CNN)-based methods. Therefore, we investigate a direct attitude estimation method that is faster, more generalized, and better suited for space applications. We propose a fast and accurate network called directly space-object-attitude-estimation network (DSOAE-Net) for estimating the attitude of space objects using monocular images. We introduce a pose representation method that is particularly suitable for space object attitude estimation, which enhances the performance of attitude estimation. We also propose a general pipeline for directly estimating attitude and explore and experiment with each stage of the pipeline. Through detailed experiments, we validate that our proposed method achieves optimal results within this framework. The proposed method demonstrates a balance between speed and accuracy, with the best accuracy results achieved among the compared methods on a weakly textured dataset.