Markers Identification for Relative Pose Estimation of an Uncooperative Target
Not on the map. Why?
Abstract (arXiv)
This paper introduces a novel method using chaser spacecraft image processing and Convolutional Neural Networks (CNNs) to detect structural markers on the European Space Agency's (ESA) Environmental Satellite (ENVISAT) for safe de-orbiting. Advanced image pre-processing techniques, including noise addition and blurring, are employed to improve marker detection accuracy and robustness. Initial results show promising potential for autonomous space debris removal, supporting proactive strategies for space sustainability. The effectiveness of our approach suggests that our estimation method could significantly enhance the safety and efficiency of debris removal operations by implementing more robust and autonomous systems in actual space missions.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 29 references that are in the map. First posted on arXiv 30 Jul 2024.
Builds on
6 of 29 references.
References · 6
- 2018Segmentation-driven 6D Object Pose Estimationcited by 104
- 2019End-to-End Wireframe Parsingcited by 72
- 2019Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinementcited by 56
- 2018Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networkscited by 34
- 2023A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospectscited by 30
- 2021Deep Learning-based Spacecraft Relative Navigation Methods: A Surveycited by 17
Built on by
Citing papers per year: 2025: 1.
1 paper.