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

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Spacecraft Pose Estimation Dataset (SPEED)

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Sharma, Sumant, Park, Tae Ha, D'Amico, Simone·2026·Stanford Digital Repository

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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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Research works printed but not linked: 5

[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. -
[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. -
[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. -
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[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: Sharma, Sumant
Park, Tae Ha
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
FWCI: 18.02
Type: journal article
Language: en
Open access: green
arxiv.org
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
Social Sciences › Political Science and International Relations › 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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