Vision-Based Relative Pose Estimation of Space Objects for Proximity Operations Using Lab-Validated Deep Learning
Matteo FORASASSI, Giordana BUCCHIONI, Lorenzo POLLINI
Abstract. This paper presents a scalable framework for real-time, 6-DOF pose estimation of uncooperative space objects using monocular cameras. We address dataset scarcity by generating mission-specific synthetic data augmented with style randomization to mitigate domain shift. A lightweight YOLOv11n-pose model extracts keypoints, which feed into a RANSAC-PnP solver for initial pose estimation, refined through an Extended Kalman Filter leveraging rigid body dynamics. Validation against synthetic sequences and laboratory experiments with a 1:1 replica demonstrates mean orientation errors of 3.9° and translation errors of 1.1%, matching state-of-the-art performance. The pipeline maintains robustness under challenging lighting conditions and domain shift, achieving ~50 fps on GPU and ~5 fps on CPU, enabling deployment on resource-constrained platforms for on-orbit servicing and debris removal missions.
Keywords
Pose, Computer Vision, YOLO, Synthetic Dataset, Extended-Kalman-Filter, Lab-Testing, Sim2Real
Published online 7/20/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Matteo FORASASSI, Giordana BUCCHIONI, Lorenzo POLLINI, Vision-Based Relative Pose Estimation of Space Objects for Proximity Operations Using Lab-Validated Deep Learning, Materials Research Proceedings, Vol. 69, pp 72-77, 2026
DOI: https://doi.org/10.21741/9781644904251-13
The article was published as article 13 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] M. Kisantal, S. Sharma, T. H. Park, D. Izzo, M. Märtens, and S. D’Amico, “Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results,” IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 5, pp. 4083–4098, Oct. 2020, doi: 10.1109/TAES.2020.2989063.
[2] T. H. Park, S. Sharma, and S. D’Amico, “Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft,” Sep. 01, 2019, arXiv: arXiv:1909.00392. https://doi.org/10.48550/arXiv.1909.00392
[3] B. Joshi et al., “DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 1777–1784. https://doi.org/10.1109/IROS45743.2020.9341201
[4] T. R. Lenhard, A. Weinmann, K. Franke, and T. Koch, “SynDroneVision: A Synthetic Dataset for Image-Based Drone Detection,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Feb. 2025, pp. 7637–7647. https://doi.org/10.1109/WACV61041.2025.00742
[5] T. H. Park and S. D’Amico, “Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap,” Advances in Space Research, vol. 73, no. 11, pp. 5726–5740, Jun. 2024, doi: 10.1016/j.asr.2023.03.036
[6] M. Forasassi, Bucchioni, G, and Pollini, Lorenzo, “Vision-based relative navigation system for autonomous proximity orbital operations,” in Materials Research Proceedings, Jul. 2024, pp. 150–153. https://doi.org/10.21741/9781644903193-33
[7] D. P. Rohe and E. M. C. Jones, “Generation of Synthetic Digital Image Correlation Images Using the Open-Source Blender Software,” Exp Tech, vol. 46, no. 4, pp. 615–631, Aug. 2022, https://doi.org/10.1007/s40799-021-00491-z
[8] P. T. Jackson, A. Atapour-Abarghouei, S. Bonner, T. P. Breckon, and B. Obara, “Style Augmentation: Data Augmentation via Style Randomization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun. 2019.
[9] D. Maji, S. Nagori, M. Mathew, and D. Poddar, “YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA: IEEE, Jun. 2022, pp. 2636–2645. https://doi.org/10.1109/cvprw56347.2022.00297
[12] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788.
[11] G. Jocher, J. Qiu, and A. Chaurasia, Ultralytics YOLO. (Jan. 2023). [Online]. Available: https://github.com/ultralytics/ultralytics
[12] V. Lepetit, F. Moreno-Noguer, and P. Fua, “EPnP: An Accurate O(n) Solution to the PnP Problem,” Int J Comput Vis, vol. 81, no. 2, pp. 155–166, Feb. 2009, https://doi.org/10.1007/s11263-008-0152-6
[13] M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” in Readings in Computer Vision, M. A. Fischler and O. Firschein, Eds., San Francisco (CA): Morgan Kaufmann, 1987, pp. 726–740. https://doi.org/10.1016/B978-0-08-051581-6.50070-2
