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Cited by 304Space Satellite Systems and ControlSpacecraft Dynamics and ControlSpace exploration and regulation

Spacecraft pose estimation dataset (speed)

Open access
Sharma, Sumant, Park, Tae Ha, D'Amico, Simone·2026·Stanford Digital Repository
DOI 10.25740/dz692fn7184
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Abstract

This repository contains Spacecraft PosE Estimation Dataset (SPEED), which is used to train and evaluate the performance of deep learning models for pose estimation of noncooperative spacecraft. SPEED consists of synthetic as well as actual camera images of a mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images are created by fusing OpenGL-based renderings of the spacecraft’s 3D model with actual images of the Earth captured by the Himawari-8 meteorological satellite. The actual camera images are created using a 7 degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of the Tango spacecraft. Custom illumination devices simulate the Earth albedo and Sun light with high fidelity to emulate the illumination conditions present in space. SPEED was used in the international competition for spacecraft pose estimation co-hosted by the Advanced Concepts Team of the European Space Agency and the Space Rendezvous Laboratory of Stanford University.

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  • Existing datasets such as SPEED have mostly relied on synthetic images for both training and validation, which fail to resemble the visual features and illumination variability inherent to target spaceborne images.

    “Existing datasets, such as Spacecraft PosE Estimation Dataset (SPEED), have so far mostly relied on synthetic images for both training and validation, which are easy to mass-produce but fail to resemble the visual features and illumination variability inherent to the target spaceborne images.”

    From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [16]

    The Introduction (p. 1) and Table I (p. 4) show that SPEED relied predominantly on synthetic images for training and testing. The real-image comparison (p. 4) identifies differences in texture and illumination, but the synthetic-dataset section (p. 3) says SPEED closely emulated illumination in actual flight imagery. The categorical claim that its images fail to resemble spaceborne illumination therefore overstates the source; the paper also does not establish the claim for existing datasets generally. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The only publicly available benchmark dataset on spacecraft pose estimation with fully labeled images from multiple sources is SPEED.

    “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 [16]

    Section III (p. 2) describes SPEED as the first publicly available spacecraft-pose-estimation dataset, with synthetic and real Tango images; it does not establish that SPEED is the only such benchmark. Table I and Section III.C (p. 4) list 15,303 images, approximately 15,300, but say only five real images are provided with training labels, so the claim that all images are fully labeled is not supported. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • SPEED consists of 15,300 images of the Tango spacecraft from the PRISMA mission.

    “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 [16]

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Flagged first, then by citations

4 of the 60 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. [9][9] S. Sharma, 'Pose estimation of uncooperative spacecraft using monocular vision and deep learning,' Ph.D. dissertation, Stanford University, Department of Aeronautics & Astronautics, Aug 2019. -
  2. [10][10] C. Beierle, 'High fidelity validation of vision-based sensors and algorithms for spaceborne navigation,' Ph.D. dissertation, Stanford University, Department of Aeronautics & Astronautics, Mar 2019. -
  3. [24][24] S. Sharma and S. D'Amico, 'Reduced-dynamics pose estimation for non-cooperative spacecraft rendezvous using monocular vision,' in 38th AAS Guidance and Control Conference, Breckenridge, Colorado , 2017. -
  4. [26][26] S. D'Amico, P. Bodin, M. Delpech, and R. Noteborn, 'PRISMA,' in Distributed Space Missions for Earth System Monitoring Space Technology Library , M. D'Errico, Ed., 2013, vol. 31, ch. 21, pp. 599-637. -
  5. [49][49] T. H. Park, S. Sharma, and S. D'Amico, 'Towards robust learningbased pose estimation of noncooperative spacecraft,' in 2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine , August 11-15 2019. -

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Authors
Sharma, S., Park, T. H., & D'Amico, S.
Affiliations
Stanford University (US)
ORCID
Venue
Stanford Digital Repository
Publisher
Stanford Digital Repository
Published
2026
Volume
56
Issue
5
Pages
4083-4098
ISSN
0018-9251 (ISSN-L)
DOI
10.25740/dz692fn7184
OpenAlex
W2988274083
Cited by
304
References
60
Citations by year
2026: 51 · 2025: 73 · 2024: 60 · 2023: 58 · 2022: 28 · 2021: 31 · 2020: 3
FWCI
18.02
Type
journal article
Language
en
Open access
Licence
creativecommons.org/licenses/by-nc-sa/4.0/legalcode
Locations
  • IEEE Transactions on Aerospace and Electronic Systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
  • Stanford Digital Repository
Funders
Air Force Office of Scientific Research
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
  • Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Space exploration and regulation
Keywords
satellite pose estimation, monocular vision, on-orbit servicing, space debris removal, synthetic images, machine learning competition, performance indicators, testbed

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