Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) methods to address this problem. However and despite promising research-stage results, major challenges preventing the use of such methods in real-life missions still stand in the way. In particular, the deployment of such computation-intensive algorithms is still under-investigated, while the performance drop when training on synthetic and testing on real images remains to mitigate. The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner. The secondary goal is to help define the limitations towards the effective deployment of DL-based spacecraft pose estimation solutions for reliable autonomous vision-based applications. To this end, the survey first summarises the existing algorithms according to two approaches: hybrid modular pipelines and direct end-to-end regression methods. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models' sizes keeping potential deployment in mind. Then, current monocular spacecraft pose estimation datasets used to train and test these methods are discussed. The data generation methods: simulators and testbeds, the domain gap and the performance drop between synthetically generated and lab/space collected images and the potential solutions are also discussed. Finally, the paper presents open research questions and future directions in the field, drawing parallels with other computer vision applications.

翻译：估计非合作航天器的姿态是一个重要的计算机视觉问题，它能够支持在轨自主视觉系统的部署，应用范围从在轨服务到空间碎片清除。遵循计算机视觉的总体趋势，越来越多的研究专注于利用深度学习方法来解决这一问题。然而，尽管在研究阶段取得了令人鼓舞的结果，但阻碍此类方法在现实任务中应用的主要挑战仍然存在。特别是，这类计算密集型算法的部署问题仍未得到充分研究，而使用合成图像训练与真实图像测试之间的性能下降问题仍有待缓解。本综述的首要目标是全面描述当前基于深度学习的航天器姿态估计方法。次要目标是明确实现基于深度学习的航天器姿态估计解决方案有效部署的局限性，以支持可靠的自主视觉应用。为此，本综述首先根据两种方法总结现有算法：混合模块化流水线和直接端到端回归方法。不仅从姿态精度方面对算法进行比较，还侧重于网络架构和模型大小，并考虑到潜在部署。随后，讨论了当前用于训练和测试这些方法的单目航天器姿态估计数据集。还讨论了数据生成方法：模拟器和测试平台、领域差距以及合成生成图像与实验室/空间采集图像之间的性能下降问题及其潜在解决方案。最后，本文提出了该领域开放的研究问题和未来方向，并与其他计算机视觉应用进行了类比。