mlsanityData to 7 Oct 2026

Deep Learning-based Spacecraft Relative Navigation Methods: A Survey

arXiv 2108.08876 ↗First posted 19 Aug 2021cs.RO cs.AI cs.CVcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 33 of its 103 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

33 of 103 references.

References · 33

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 7; 2024: 3; 2025: 1; 2026: 1.

17 papers.

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  1. 2023A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospectscited by 30
  2. 2023Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directionscited by 20
  3. 2022AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learningcited by 10
  4. 2023SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detectioncited by 5
  5. 2023Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Reviewcited by 5
  6. 2022Lessons from a Space Lab -- An Image Acquisition Perspectivecited by 3
  7. 2022Autonomous crater detection on asteroids using a fully-convolutional neural networkcited by 2
  8. 2025Motion Aware ViT-based Framework for Monocular 6-DoF Spacecraft Pose Estimationcited by 2
  9. 2022DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setupcited by 1
  10. 2024Markers Identification for Relative Pose Estimation of an Uncooperative Targetcited by 1
  11. 2024Explainable Convolutional Networks for Crater Detection and Lunar Landing Navigationcited by 1
  12. 2024Aligning Motion-Blurred Images Using Contrastive Learning on Overcomplete Pixelscited by 1
5 more
  1. 2026Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Constructioncited by 1
  2. 2022Boulders Identification on Small Bodies Under Varying Illumination Conditionscited by 0
  3. 2023A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Imagescited by 0
  4. 2023Orbital AI-based Autonomous Refuelling Solutioncited by 0
  5. 2023Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvouscited by 0