The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Vision-Based Relative Pose Estimation for Autonomous Rendezvous And Docking
Abstract
Autonomous rendezvous and docking is necessary for planned space programs such as DARPA ASTRO, NASA MSR, ISS assembly and servicing, and other rendezvous and proximity operations. Estimation of the relative pose between the host platform and a resident space object is a critical ability. We present a model-based pose refinement algorithm, part of a suite of algorithms for vision-based relative pose estimation and tracking. Algorithms were tested in high-fidelity simulation and stereo-vision hardware test bed environments. Testing indicated that in most cases, the model-based pose refinement algorithm can handle initial attitude errors up to about 20 degrees, range errors exceeding 10% of range, and transverse errors up to about 2% of range. Preliminary point tests with real camera sequences of a 1/24 scale Magellan satellite model using a simple fixed-gain tracking filter showed potential tracking performance with mean errors of < 3 degrees and < 2% of range.
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
“two image simulation tools have so far been specifically developed to support vision-based navigation in space scenes (e.g. Martian surface, asteroid landing): the early PANGU [30] used by ESA and the more comprehensive Airbus internal simulator: SurRender [31], which supports ray tracing and very large datasets.”
From Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering (2020) · cites this paper as [30]
Citations
Cites
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Closed-form solution of absolute orientation using unit quaternions
Horn, B. K. P. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.000629 · Cited by 4,165
Least-Squares Fitting of Two 3-D Point Sets
Arun, K. S., Huang, T. S., & Blostein, S. D. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767965 · Cited by 3,952
Shape and motion from image streams under orthography: a factorization method
Tomasi, C., & Takeo Kanade, A. (1992) · International Journal of Computer Vision · doi:10.1007/bf00129684 · Cited by 2,879
Active Appearance Models Revisited
Matthews, I., & Baker, S. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029666.37597.d3 · Cited by 1,538
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
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
Burkhardt, H., & Neumann, B. (1998) · Lecture notes in computer science · doi:10.1007/bfb0054729 · Cited by 378
Visual tracking of known three-dimensional objects
Gennery, D. B. (1992) · International Journal of Computer Vision · doi:10.1007/bf00126395 · Cited by 262
Ohba, K., & Ikeuchi, K. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.615453 · Cited by 169
Flagged first, then by citations
1 of the 21 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–128 of 128
RELATIVE MOTION PARAMETERS ESTIMATION OF A NON-COOPERATIVE SPACECRAFT FROM VISUAL INFORMATION
Salnikov, N. N., Мельничук, С. В., Gubarev, V. F., Maksymyuk, L. V., & Shevchenko, V. N. (2023) · Kosmìčna nauka ì tehnologìâ · doi:10.15407/knit2023.03.016 · Cited by 1
Non-cooperative Satellite Rotation Estimation Based on Monocular Vision
He, J., Yi, J., & Xu, L. (2024) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs61748.2024.00056 · Cited by 1
“Kelsey et al. [6] proposed a model-based target tracking algorithm, in which, on the premise of having a 3D model of the target spacecraft, the target edge features extracted from the 2D image are matched with the 3D model wireframe and solved using a nonlinear optimization function to obtain the…”
MethodVision Based Relative Navigation
Accardo, D., Fasano, G., & Grassi, M. (2012) · Springer eBooks · doi:10.1007/978-1-4614-4541-8_7 · Cited by 1
Visual detection of air-to-air refueling drogue
Krause, S., & Aydin, B. (2015) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2015.7152318 · Cited by 1
“time [s] [1] [2] [3] [4] [5] [6] [7] [8] [9] approx.”
BackgroundUsing Video Images for Determining Relative Disposition of Two Spacecraft
Simakov, V., Gubarev, V. F., Salnikov, N. N., & Melnichuk, S. V. (2016) · Kibernetika i vyčislitelʹnaâ tehnika · doi:10.15407/kvt185.03.035 · Cited by 1
Zhao, S., Zhang, D., Zhou, Y., & Shu, X. (2019) · Optical Metrology and Inspection for Industrial Applications · doi:10.1117/12.2536959 · Cited by 1
“Stereovision is the third method to measure the displacement angle, which can improve the past two-dimensional image analysis into three-dimensional landscape analysis[7-9].”
MethodSegmentation-based orbiting satellite tracking
Sun, Y., Li, P., & Wan, X. (2020) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1145/3381271.3381291 · Cited by 1
“[3, 4].”
Station-keeping using Perception and Relative Image-based Navigation and Tracking (SPRINT) for UAS
Bošković, J. D., Diel, D. D., & Jackson, J. A. (2021) · AIAA SciTech Forum · doi:10.2514/6.2021-1759 · Cited by 1
Relative navigation for a malfunctioned spacecraft by using an adaptive state observer
Yu, F., & He, Z. (2015) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2015.7320699
Zhang, Y. J., Xu, Y., Li, J., Li, Z., & Wu, S. (2007) · doi:10.1109/ieem.2007.4419228
“1-4244-1529-2/07/$25.00 ©2007 IEEE fuel wastage, relative position and attitude parameters [ 2 ,6,7,9].”
BackgroundInfluentialA videometrics method of cooperative object pose-measurement
Zhang, Y., Shang, Y., Ou, J., & Ma, X. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2197412
“Researchers presented some methods to measure a docking object’s pose from images ([5-10]).”
MethodReal-time robust motion tracking using 3D point cloud for space debris removal
Hirano, D., & Kato, H. (2016) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2016.7500598
A space-based stereovision system for relative state estimation of space debris
Cai, H., Yang, Y., Wu, S., Afful, M., & Zhang, K. F. (2016) · AIAA/AAS Astrodynamics Specialist Conference and Exhibit · doi:10.2514/6.2016-5515
Relative Navigation Determination in the Capture of Non-Cooperative Targets
Martinez, H. C. G., & Boge, T. (2013)
Saliency based Semi-supervised Learning for Orbiting Satellite TrackingPreprint
Li, P., Sun, Y., & Wan, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03656
“The visual perception information of the target satellite will help our understanding the state of the satellite for decision making in further step, such as rendezvous and docking [3, 4], repairing and manipulation [5], etc.”
BackgroundSpacecraft Pose Estimation Based on Different Camera ModelPreprint
Lidong, M., Chi, G., & Zhao, Z. (2020) · Research Square · doi:10.21203/rs.3.rs-62297/v1
Airborne Vision Based Target Motion State Estimation for UAV Aerial Docking
Li, R., He, S., Song, T., & Tao, H. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-6613-2_427
Erkeç, T. Y. (2023) · International Conference on Recent Advances in Space Technologies · doi:10.1109/rast57548.2023.10197922
“The movements of the target satellite can be controlled using various techniques, including camera-based tracking and Simultaneous Localization and Mapping (SLAM) techniques [7,8].”
Method6-DOF Pose Estimation for Spacecraft Based on Soft Classification of Euler Angles Optimization
Tan, Q., Zhou, D. X., Zhang, Z., Zhang, Z., Liu, H., & Shao, X. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10451125
Vision-Only-Based Control of Approaching Disabled Satellites via Deep Learning
Li, P., Dong, Y., Li, H., Deng, Y. C., & Liew, Y. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3381128
“These methods primarily feature a sequential two-step design, which entails attitude acquisition in the first step and attitude tracking in the second step [5].”
MethodA Sparse Semantic Reconstruction Method for Spacecraft for Collaborative Awareness
Wu, B., Zeng, Y., Zhao, X., Gao, Z., & Wang, W. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-3324-8_56
Pixel Voting Network-Based Monocular Pose Estimation for Space Non-Cooperative Targets
Zhang, Y., Wang, J., Hou, B., Bin, Y., Zhou, H., & Zhou, X. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865108
“Traditional methods are classified as pose estimation methods based on strong geometric features [5-10], and template matching based pose estimation method [11, 12].”
Chen, L., Yi, J., Wu, B., & Su, L. (2025) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.3057839
Yuan, H., Wu, J., Shao, B., & Kang, G. (2025) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2025.3633216
COMET: Continuous-Time Trajectory-Guided Temporal Modeling for Spacecraft Pose Estimation
Qi, Z., Fan, H., Zou, Y., & Zhang, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463426
Metadata
- Authors
- Kelsey, J. M., Byrne, J., Cosgrove, M., Seereeram, S., & Mehra, R. K.
- Affiliations
- Scientific Systems (United States) (US)
- ORCID
- Byrne, J.
- Venue
- 2006 IEEE Aerospace Conference
- Publisher
- IEEE
- Published
- 2006-08-02
- Pages
- 1-20
- ISSN
- 1095-323X (ISSN-L)
- OpenAlex
- W1581087960
- Cited by
- 130
- References
- 21
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 11 · 2023: 12 · 2022: 8 · 2021: 7 · 2020: 12 · 2019: 13 · 2018: 6 · 2017: 4 · 2016: 12 · 2015: 9 · 2014: 8 · 2013: 6 · 2012: 2
- FWCI
- 3.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Aerospace Conference (AERO) (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 › Advanced Vision and Imaging
- Keywords
- relative pose estimation, autonomous rendezvous, spacecraft docking, visual object tracking, stereo vision, proximity operations, attitude estimation, range estimation
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