Next Generation Spacecraft Pose Estimation Dataset (SPEED+)
Abstract/Contents
- Abstract
SPEED+ is the next-generation dataset for spacecraft pose estimation with specific emphasis on the robustness of Machine Learning (ML) models across the domain gap. Similar to its predecessor, SPEED+ consists of images of the Tango spacecraft from the PRISMA mission. SPEED+ consists of three different domains of imageries from two distinct sources. The first source is the OpenGL-based Optical Stimulator camera emulator software of Stanford’s Space Rendezvous Laboratory (SLAB), which is used to create the synthetic domain comprising 59,960 synthetic images. The labeled synthetic domain is split into 80:20 train/validation sets and is intended to be the main source of training of an ML model. The second source is the Testbed for Rendezvous and Optical Navigation (TRON) facility at SLAB, which is used to generate two simulated Hardware-In-the-Loop (HIL) domains with different sources of illumination: lightbox and sunlamp. Specifically, these two domains are constructed using realistic illumination conditions using lightboxes with diffuser plates for albedo simulation and a sun lamp to mimic direct high-intensity homogeneous light from the Sun. Compared to synthetic imagery, they capture corner cases, stray lights, shadowing, and visual effects in general which are not easy to obtain through computer graphics. The lightbox and sunlamp domains are unlabeled and thus intended mainly for testing, representing a typical scenario in developing a spaceborne ML model in which the labeled images from the target space domain are not available prior to deployment. SPEED+ is made publicly available to the aerospace community and beyond as part of the second international Satellite Pose Estimation Competition (SPEC2021) co-hosted by SLAB and the Advanced Concepts Team (ACT) of the European Space Agency.
The construction of the TRON testbed was partly funded by the U.S. Air Force Office of Scientific Research (AFOSR) through the Defense University Research Instrumentation Program (DURIP) contract FA9550-18-1-0492, titled High-Fidelity Verification and Validation of Spaceborne Vision-Based Navigation. The SPEED+ dataset is created using the TRON testbed by SLAB at Stanford University. The post-processing of the raw images is reviewed by ACT to meet the quality requirement of SPEC2021.
Description
Type of resource | Dataset, still image |
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Date created | [ca. July 1, 2021] |
Creators/Contributors
Author | Park, Tae Ha | |
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Author | Märtens, Marcus | |
Author | Lecuyer, Gurvan | |
Author | Izzo, Dario | |
Author | D'Amico, Simone |
Subjects
Subject | Satellites |
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Subject | Pose Estimation |
Subject | Computer vision |
Subject | Machine learning |
Subject | Domain Gap |
Genre | Data |
Genre | Image |
Genre | Data sets |
Genre | Dataset |
Bibliographic information
Related item |
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DOI | https://doi.org/10.25740/wv398fc4383 |
Location | https://purl.stanford.edu/wv398fc4383 |
Access conditions
- License
- This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 4.0 International license (CC BY-NC-SA).
Preferred citation
- Preferred citation
- Park, T. H., Märtens, M., Lecuyer, G., Izzo, D., D'Amico, S. (2021). Next Generation Spacecraft Pose Estimation Dataset (SPEED+). Stanford Digital Repository. Available at https://purl.stanford.edu/wv398fc4383. https://doi.org/10.25740/wv398fc4383
Collection
Stanford Research Data
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