Spacecraft pose estimation is a key task to enable space missions in which two spacecrafts must navigate around each other. Current state-of-the-art algorithms for pose estimation employ data-driven techniques. However, there is an absence of real training data for spacecraft imaged in space conditions due to the costs and difficulties associated with the space environment. This has motivated the introduction of 3D data simulators, solving the issue of data availability but introducing a large gap between the training (source) and test (target) domains. We explore a method that incorporates 3D structure into the spacecraft pose estimation pipeline to provide robustness to intensity domain shift and we present an algorithm for unsupervised domain adaptation with robust pseudo-labelling. Our solution has ranked second in the two categories of the 2021 Pose Estimation Challenge organised by the European Space Agency and the Stanford University, achieving the lowest average error over the two categories.
翻译:由于空间环境的成本和困难,空间航天器在空间条件下的图像缺乏真正的培训数据。这促使引入了3D数据模拟器,解决了数据提供问题,但在培训(来源)和测试(目标)领域之间造成了很大的差距。我们探索了一种方法,将3D结构纳入航天器构成估算管道,以提供强度域变迁的稳健性,我们用强有力的伪标签提出了无监督域适应的算法。我们的解决方案在欧洲航天局和斯坦福大学组织的2021年“ose Estimation 挑战”的两类中排名第二,在两类中达到了最低的平均误差。