The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Relative pose estimation for cylinder-shaped spacecrafts using single image
Abstract
This paper describes a novel framework to determine the relative pose and range of a solid-of-revolution-shaped spacecraft from a single image without any artificial beacons. The translation and the symmetry axis of the spacecraft can be estimated from the imaged cross sections of the spacecraft body. Then the pose and range of the spacecraft are fully determined by means of the images of its solar panels and asymmetric feature. Our method has been validated by both synthetic and real images.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“but its application is not feasible for the pose estimation of a spacecraft of generic shape.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [14]
In the citing paper
“Liu and Hu [14] presented a 265 robust method based on ellipses extraction for cylinder-shaped spacecraft”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [14]
Citations
Cites
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
Multiple view geometry in computer vision
Opower, H. (2002) · Optics and Lasers in Engineering · doi:10.1016/s0143-8166(01)00145-2 · Cited by 3,044
Cootes, T. F., Edwards, G. J., & Taylor, C. (1998) · Lecture notes in computer science · doi:10.1007/bfb0054760 · Cited by 2,210
Three-dimensional object recognition from single two-dimensional images
Lowe, D. G. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90070-1 · Cited by 1,283
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
Black, M. J., & Jepson, A. D. (1998) · International Journal of Computer Vision · doi:10.1023/a:1007939232436 · Cited by 1,038
Fitting parameterized three-dimensional models to images
Lowe, D. G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.134043 · Cited by 838
Algebraic Projective Geometry.
Atchison, W. F., Semple, J. G., & Kneebone, G. T. (1953) · American Mathematical Monthly · doi:10.2307/2307420 · Cited by 708
Real-time visual tracking of complex structures
Drummond, T., & Cipolla, R. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017620 · Cited by 598
Recursive 3-D motion estimation from a monocular image sequence
Broida, T. J., Chandrashekhar, S., & Chellappa, R. (1990) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.55557 · Cited by 302
Visual tracking of known three-dimensional objects
Gennery, D. B. (1992) · International Journal of Computer Vision · doi:10.1007/bf00126395 · Cited by 262
Research works printed but not linked: 4
- zhang, 1989, Analytic Geometry, 510
- junkins, 1999, Vision-based navigation for rendezvous, docking and proximity operations, InAAS Guidance and Control Conference
- petit, 2012, Vision-based detection and tracking for space navigation, International Symposium on Artificial Intelligence Robotics and Automation in Space
- chen, 2005, The optimization theory and algorithm[M], 405
Cited by
Sorted by citations
Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2017) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.07.001 · Cited by 330
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
“Previous monocular solutions [6–11] to spacecraft tracking and pose estimation rely on model-based approaches (e.”
BackgroundNeural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous
Sharma, S., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2999148 · Cited by 196
Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156
Pose estimation for spacecraft relative navigation using model-based algorithms
Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2017) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2017.2650785 · Cited by 127
Metadata
- Authors
- Liu, C., & Hu, W.
- Affiliations
- Beihang University (CN)
- ORCID
- Venue
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 2014-10
- Volume
- 50
- Issue
- 4
- Pages
- 3036-3056
- ISSN
- 0018-9251 (ISSN-L)
- OpenAlex
- W2064607980
- Cited by
- 101
- References
- 49
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 9 · 2023: 14 · 2022: 9 · 2021: 10 · 2020: 8 · 2019: 14 · 2018: 13 · 2017: 5 · 2016: 3 · 2015: 1 · 2014: 1
- FWCI
- 4.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Aerospace and Electronic Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
- Keywords
- relative pose estimation, single image, solids of revolution, solar panels, cross-section images
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
