The DOI matches its Crossref record; 170 of the 184 references it cites were found in a registry or an index.
A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations
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In the citing paper
“Recent surveys on the topic focused on a comparative assessment of the pose estimation solvers [11] or provided a broader review on cooperative as well as uncooperative targets by including monocular-as well as stereo-and LIDAR-based systems [10].”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [10]
In the citing paper
“In particular, this architecture shows improvements with respect to previous IP and pose estimation techniques [15,11,10].”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [10]
In the citing paper
“while on the other hand, stereo cameras and LIDAR sensors are less flex-45 ible and less convenient in terms of operational range, mass, power consumption and processing power [10].”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [10]
In the citing paper
“Several approaches exist in literature to estimate the target moment of inertia with stereo cameras or other active sensors such as LIDARs.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [10]
Citations
Cites
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
A Method for Registration of 3-D Shapes
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Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
Proceedings of IEEE International Conference on Computer Vision
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ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037
Use of the hough transformation to detect lines and curves in pictures
Duda, R. O., & Hart, P. E. (1972) · Communications of the ACM · doi:10.1145/361237.361242 · Cited by 6,633
Tsai, R. (1987) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1987.1087109 · Cited by 5,890
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Research works printed but not linked: 5
- Alfriend, 2009, vol. 2
- Cheng, 2002, vol. 1, 351
- Bodin, 2012, The prisma formation flying demonstrator: overview and conclusions from the nominal mission, Adv. Astronaut. Sci., 144, 441
- Junkins, 2011, Small body proximity sensing with a novel HD3-d LADAR system, Adv. Astronaut. Sci., 141, 341
- Liu, 2016, Relative pose estimation for cylinder-shaped spacecrafts using single image, IEEE Trans. Aerosp. Electron. Syst., 50, 3036, 10.1109/TAES.2014.120757 · DOI · Paper page
Cited by
Sorted by citations
On-orbit service (OOS) of spacecraft: A review of engineering developments
Li, W., Cheng, D.-Y., Liu, X., Wang, Y., Shi, W., Tang, Z., Gao, F., Zeng, F., Chai, H.-Y., Luo, W., et al. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.01.004 · Cited by 448
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
“As surveyed in [3], according to the specific operation scenario, the targets may be either: (i) cooperative if they use a dedicated radio-link, fiducial markers or retroreflectors to aid pose determination or (ii) non-cooperative with either unknown or known geometry.”
BackgroundRobotic Manipulation and Capture in Space: A Survey
Papadopoulos, E. G., Aghili, F., Ma, O., & Lampariello, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.686723 · Cited by 226
Advances in Space Robots for On‐Orbit Servicing: A Comprehensive Review
Ma, B., Jiang, Z., Liu, Y., & Xie, Z. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200397 · Cited by 169
Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156
Metadata
- Authors
- Opromolla, R., Fasano, G., Rufino, G., & Grassi, M.
- Affiliations
- University of Naples Federico II (IT)
- Corresponding author
- Opromolla, Roberto
- Venue
- Progress in Aerospace Sciences
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 93
- Pages
- 53-72
- ISSN
- 0376-0421 (ISSN-L)
- OpenAlex
- W2739206226
- Cited by
- 330
- References
- 184
- Citations by year
- 2026: 31 · 2025: 52 · 2024: 54 · 2023: 37 · 2022: 26 · 2021: 49 · 2020: 31 · 2019: 29 · 2018: 20 · 2017: 1
- FWCI
- 20.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Progress in Aerospace Sciences (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Space Technology and Applications
- Keywords
- close proximity operations, cooperative targets, non-cooperative targets, relative navigation, electro-optical sensors, formation flying, on-orbit servicing, active debris removal, sensor architecture, target geometry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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