论文标题
航天器基于无监督的域适应和3D引导的损失组合的估计
Spacecraft Pose Estimation Based on Unsupervised Domain Adaptation and on a 3D-Guided Loss Combination
论文作者
论文摘要
航天器姿势估计是实现空间任务的关键任务，其中两个航天器必须相互浏览。姿势估计的当前最新算法采用数据驱动技术。但是，由于与太空环境相关的成本和困难，没有用于在太空条件下成像的航天器的真实培训数据。这促使引入3D数据模拟器，解决了数据可用性问题，但在培训（源）和测试（目标）域之间引入了很大的差距。我们探索了一种将3D结构纳入航天器姿势估计管道的方法，以提供强度域移位的鲁棒性，并且我们提出了一种算法，用于使用稳健的伪标记，以适应无监督域的适应性。我们的解决方案在欧洲航天局和斯坦福大学组织的2021姿势估计挑战赛的两类中排名第二，在这两类中达到了最低的平均误差。
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.
