The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Pose estimation of an uncooperative spacecraft from actual space imagery
Abstract
This paper addresses the preliminary design of a spaceborne monocular vision-based navigation system for on-orbit-servicing and formation-flying applications. The aim is to estimate the pose of a passive space resident object using its known three-dimensional model and single low-resolution two-dimensional images collected on-board the active spacecraft. In contrast to previous work, no supportive means are available on the target satellite (e.g., light emitting diodes) and no a-priori knowledge of the relative position and attitude is available (i.e., lost-in-space scenario). Three fundamental mechanisms – perceptual organisation, true perspective projection, and random sample consensus – are exploited to overcome the limitations of monocular passive optical navigation in space. The preliminary design is conducted and validated making use of actual images collected in the frame of the PRISMA mission at about 700 km altitude and 10 m inter-spacecraft separation.
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In the citing paper
“The HIL images of SPEED+ involve a half-scale mockup model of the Tango spacecraft from the PRISMA mission [17,18].”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [17]
In the citing paper
“To the best of the authors' knowledge, the only publicly available benchmark dataset on spacecraft pose estimation with fully labeled images from multiple sources is the Spacecraft PosE Estimation Dataset (SPEED) [16] which consists of 15,300 images of the Tango spacecraft from the PRISMA mission [17, 18].”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [17]
In the citing paper
“To the best of the authors' knowledge, the only publicly available benchmark dataset on spacecraft pose estimation with fully labeled images from multiple sources is the Spacecraft PosE Estimation Dataset (SPEED) [16] which consists of 15,300 images of the Tango spacecraft from the PRISMA mission [17, 18].”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [17]
In the citing paper
“Furthermore, it did not prove to be robust with respect to the spacecraft symmetry.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
In the citing paper
“but it lacked of robustness to illumination and background conditions.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
In the citing paper
“Its method was tested with the PRISMA image dataset and proved to be flexible with respect to the spacecraft 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 [15]
In the citing paper
“D'Amico et al. [15] used the same feature detection and extraction methods in [13] in combination with a Low-Pass Filter (LPF).”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
In the citing paper
“Also, the limited amount of realistic space images available in [15], [24] and [9] could not represent all the 450 challenging orbital scenarios for which a specific camera-target-Sun-Earth geometry would affect the pose estimation accuracy.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [15]
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 [15]
In the citing paper
“SPNv2 is also evaluated on prisma25 which contains 25 labeled spaceborne images of Tango acquired during the rendezvous phase of the PRISMA mission (D'Amico et al., 2013, 2014).”
From Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap (2024) · cites this paper as D'Amico et al., 2014
In the citing paper
“The SPEED+ [22] dataset comprises images of the Tango spacecraft from the PRISMA [49] mission, consisting of three distinct domains, i.e., synthetic, lightbox, and sunlamp.”
From Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints (2024) · cites this paper as [49]
Citations
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
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
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Torr, P. H. S., & Zisserman, A. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0832 · Cited by 2,402
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
Model-based object pose in 25 lines of code
Dementhon, D. F., & Davis, L. S. (1995) · International Journal of Computer Vision · doi:10.1007/bf01450852 · Cited by 1,034
Perceptual organization and visual recognition
(1985) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(85)90041-6 · Cited by 1,033
Spaceborne Autonomous Formation-Flying Experiment on the PRISMA Mission
D'Amico, S., Ardaens, J.-S., & Larsson, R. (2012) · Journal of Guidance, Control, and Dynamics · doi:10.2514/1.55638 · Cited by 173
Self-citationFlight Dynamics Challenges of the German On-Orbit Servicing Mission DEOS
Rupp, T., Boge, T., Kiehling, R., & Sellmaier, F. (2009) · elib (German Aerospace Center) · Cited by 67
Prisma Formation Flying Demonstrator: Overview and Conclusions from the Nominal Mission
Bodin, P., Noteborn, R., Larsson, R., Karlsson, T., D’Amico, S., Ardaens, J.-S., Delpech, M., & Bergès, J. (2012) · elib (German Aerospace Center) · Cited by 58
Cropp, A. (2001) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 29
Final commissioning of the PRISMA GPS navigation system
Ardaens, J.-S., D’Amico, S., & Montenbruck, O. (2012) · Journal of Aerospace Engineering Sciences and Applications · doi:10.7446/jaesa.0403.10 · Cited by 28
Formation flying and mission design for Proba-3
Landgraf, M., & Mestreau-Garreau, A. (2012) · Acta Astronautica · doi:10.1016/j.actaastro.2012.03.028 · Cited by 16
Autonomous spacecraft docking using a computer vision system
Ho, C.-C. J., & McClamroch, N. H. (2005) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1992.371650 · Cited by 14
An Analysis of Lowe's Model-based Vision System
McIvor, A. M. (1988) · Alvey Vision Conference (AVC) · doi:10.5244/c.2.12 · Cited by 7
Using an autonomous star tracker as formation flying sensor
Jørgensen, J. L., Denver, T., & Jørgensen, P. S. (2004) · Cited by 7
Self-citation
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
Spacecraft pose estimation dataset (speed)
Sharma, S., Park, T. H., & D'Amico, S. (2026) · Stanford Digital Repository · doi:10.25740/dz692fn7184 · Cited by 304
Self-citationSatellite 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
“[16] were one of the first to publish pose estimation results using the Hough transform and the Canny edge detector on spaceborne images captured during the rendezvous phase of the PRISMA mission [16], [26].”
Neural 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
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
Metadata
- Authors
- D', S., Amico, N. A., Benn, M., & Jørgensen, J. L.
- Affiliations
- Stanford University (US)
- DTU Space (DK)
- Venue
- International Journal of Space Science and Engineering
- Publisher
- Inderscience Publishers
- Published
- 2014
- Volume
- 2
- Issue
- 2
- Pages
- 171
- ISSN
- 2048-8459 (ISSN-L)
- 2048-8467
- OpenAlex
- W2594388292
- Cited by
- 132
- References
- 16
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 21 · 2023: 20 · 2022: 12 · 2021: 17 · 2020: 10 · 2019: 10 · 2018: 11 · 2017: 3 · 2016: 5
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Space Science and Engineering (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
- pose estimation, on-orbit servicing, formation flying, perceptual organization, perspective projection, RANSAC, PRISMA mission, spaceborne imaging
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