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]
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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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