  Autonomous spacecraft relative navigation technology has been planned for and
applied to many famous space missions. The development of on-board electronics
systems has enabled the use of vision-based and LiDAR-based methods to achieve
better performances. Meanwhile, deep learning has reached great success in
different areas, especially in computer vision, which has also attracted the
attention of space researchers. However, spacecraft navigation differs from
ground tasks due to high reliability requirements but lack of large datasets.
This survey aims to systematically investigate the current deep learning-based
autonomous spacecraft relative navigation methods, focusing on concrete orbital
applications such as spacecraft rendezvous and landing on small bodies or the
Moon. The fundamental characteristics, primary motivations, and contributions
of deep learning-based relative navigation algorithms are first summarised from
three perspectives of spacecraft rendezvous, asteroid exploration, and terrain
navigation. Furthermore, popular visual tracking benchmarks and their
respective properties are compared and summarised. Finally, potential
applications are discussed, along with expected impediments.
