Deep learning for real-time satellite pose estimation on tensor processing units
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Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.
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Existing state-of-the-art spacecraft pose estimation methods often rely on iterative PnP-based algorithms, which are computationally intensive and ill-suited for real-time deployment on resource-constrained edge devices.
“Existing state-of-the-art methods often rely on iterative Perspective-n-Point (PnP)-based algorithms, which are computationally intensive and ill-suited for real-time deployment on resource-constrained edge devices.”
From FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation (2026) · cites this paper as [21]
The introduction says three of the four top-scoring challenge entries used off-the-shelf PnP solvers, and Methods §B describes the authors’ own EPnP solver with an optimization step. The paper discusses computational burden and limited suitability for low-power devices, but attributes that burden chiefly to neural-network pipelines, not specifically to iterative PnP; its own PnP-based pipeline achieves real-time performance on embedded hardware. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe algorithms [44,54,99] and the SLAB Baseline [51] provide a good trade-off in terms of performance and number of parameters.
“The results show that the algorithms [44,54,99] and the SLAB Baseline [51] provide a good trade-off in terms of the performance and the number of parameters.”
From A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects (2023) · cites this paper as [54]
Among current research works, only a small fraction of developed algorithms are tested and evaluated on edge systems for space deployment.
“Among the current research works, only a small fraction of the developed algorithms are tested and evaluated on edge systems for space deployment [43,54,73].”
From A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects (2023) · cites this paper as [54]
The algorithms [44,54,99] and the SLAB Baseline [51] provide a good trade-off in terms of performance and number of parameters, with position and orientation errors of less than 0.30 m and 4 degrees, respectively, while using fewer than 20 million parameters.
“The results show that the algorithms[44,54,99] and the SLAB Baseline[51] provide a good trade-off in terms of the performance and the number of parameters, as shown by the red regions of each plot.”
From A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects (2023) · cites this paper as [54]
Many researchers have adopted approaches that combine keypoints detection with existing Perspective-n-Point (PnP) pose estimation solvers.
“Consequently, many researchers [8,9,11,14-16] have adopted approaches that combine keypoints detection with existing Perspective-n-Point (PnP) pose estimation solvers.”
From Pose Estimation for Cross-Domain Non-Cooperative Spacecraft Based on Spatial-Aware Keypoints Regression (2024) · cites this paper as [15]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works
EPnP: An Accurate O(n) Solution to the PnP Problem
Lepetit, V., Moreno-Noguer, F., & Fua, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0152-6 · Cited by 3,298 · see citing works
Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results
Kisantal, M., Sharma, S., Park, T. H., Izzo, D., Martens, M., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2989063 · Cited by 304 · see citing works
Spacecraft pose estimation dataset (speed)
Sharma, S., Park, T. H., & D'Amico, S. (2026) · Stanford Digital Repository · doi:10.25740/dz692fn7184 · Cited by 304
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197244 · Cited by 229 · see citing works
Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks
Sharma, S., Beierle, C., & D'Amico, S. (2018) · 2018 IEEE Aerospace Conference · doi:10.1109/aero.2018.8396425 · Cited by 207 · see citing works
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00343 · Cited by 203 · see citing works
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., Kindermans, P.-J., Tan, M., Singh, V., & Chen, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00382 · Cited by 193 · see citing works
Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156 · see citing works
Wide-Depth-Range 6D Object Pose Estimation in Space
Hu, Y., Speierer, S., Jakob, W., Fua, P., & Salzmann, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01561 · Cited by 131 · see citing works
Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous
Sharma, S., Ventura, J., & D’Amico, S. (2018) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34124 · Cited by 128 · see citing works
Deep learning-based spacecraft relative navigation methods: A survey
Song, J., Rondao, D., & Aouf, N. (2022) · Acta Astronautica · doi:10.1016/j.actaastro.2021.10.025 · Cited by 106 · see citing works
Revisiting Monocular Satellite Pose Estimation With Transformer
Wang, Z., Zhang, Z., Sun, X., Li, Z., & Yu, Q. (2022) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2022.3161605 · Cited by 59 · see citing works
Monocular Relative Pose Estimation Pipeline for Uncooperative Resident Space Objects
Piazza, M., Maestrini, M., & Lizia, P. D. (2022) · Journal of Aerospace Information Systems · doi:10.2514/1.i011064 · Cited by 37 · see citing works
A Lightweight Model for Satellite Pose Estimation
Carcagnì, P., Leo, M., Spagnolo, P., Mazzeo, P. L., & Distante, C. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-06427-2_1 · Cited by 6 · see citing works
A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery
Schubert, C., Black, K., Fonseka, D., et al. (2021) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero50100.2021.9438232 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- [2]2. Capuano, V., Cuciniello, G., Pesce, V., Opromolla, R., Sarno, S., Lavagna, M., Grassi, M., Corraro, F., Capuano, G., Tabacco, P., Meta, F., Battagliere, M., and Tuozzi, A., "VINAG: A Highly Integrated System for Autonomous On-Board Absolute and Relative Spacecraft Navigation," Proceedings of the 4S Symposium, European Space Agency, 2018, pp. 1-20, https://atpi.eventsair.com/QuickEventWebsitePortal/ 4s2018/4s/ExtraContent/ContentSubPage?page=1&subPage=1.
- [4]4. Sharma, S., and D'Amico, S., "Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks," 2019 AAS/AIAA Astrodynamics Specialist Conference, American Astronautical Soc. Paper 19-350, Springfield, VA, 2019, pp. 1-20.
- [6]6. Park, T.H., Sharma, S., and D'Amico, S., "Towards Robust Learning Based Pose Estimation of Noncooperative Spacecraft," 2019 AAS/AIAA Astrodynamics Specialist Conference, American Astronautical Soc. Paper 19-840, Springfield, VA, 2019, pp. 1-20.
- [12]12. Posso, J., Bois, G., and Savaria, Y., "Mobile-URSONet: An Embed- dable Neural Network for Onboard Spacecraft Pose Estimation," Pro- ceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, New York, 2022, pp. 794-798.
- [13]13. Black, K., Shankar, S., Fonseka, D., Deutsch, J., Dhir, A., and Akella, M. R., "Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery," 31st AAS/AIAA Space Flight Mechanics Meeting, American Astronautical Soc. Paper 21-283, Springfield, VA, 2021, pp. 1-16.
- [18]18. Goodwill, J., Crum, G., MacKinnon, J., Brewer, C., Monaghan, M., Wise, T., and Wilson, C., "NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis," Proceedings of the Small Satellite Conference, No. SSC21-VII-08, Univ. of Utah, 2021, https://digitalcommons.usu.edu/smallsat/.
- [20]20. Tan, M., and Le, Q., "EfficientNnet: Rethinking Model Scaling for Convolutional Neural Networks," International Conference on Machine Learning, Univ. of Cambridge, Cambridge, 2019, pp. 6105- 6114, http://proceedings.mlr.press/v97/.
Cited by
Sorted by citations
Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121 · see citing works
Wang, Z., Liu, Y., & Zhang, E. (2024) · Aerospace · doi:10.3390/aerospace11110948 · Cited by 5 · see citing works
FastPose-ViT: A Vision Transformer for Real-Time Spacecraft Pose Estimation
Ancey, P., Price, A., Javed, S., & Salzmann, M. (2026) · 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv61042.2026.00760
“…approaches have achieved high accuracy using hybrid pipelines that combine keypoint detection with iterative Perspective-n-Point (PnP) solvers [10, 13, 21, 25, 35], these methods are inherently slow and poorly suited to real-time deployment on edge computing platforms due to their iterative nature.”
- Authors
- Lotti, A., Modenini, D., Tortora, P., Saponara, M., & Perino, M. A.
- Affiliations
- University of Bologna (IT)
- Venue
- Journal of Spacecraft and Rockets
- Published
- 2023-05
- Volume
- 60
- Issue
- 3
- Pages
- 1034-1038
- ISSN
- 0022-4650 (ISSN-L)
- 1533-6794
- OpenAlex
- W4322494230
- Cited by
- 39
- References
- 19
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 10 · 2023: 3
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- green
- arc.aiaa.org
- Licence
- other-oa
- Locations
- Journal of Spacecraft and Rockets (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- Funders
- Thales Alenia Space Italia (0000122)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Keywords
- satellite pose estimation, real-time pose estimation, tensor processing units, deep learning, spacecraft attitude determination, edge computing
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