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Project: Spacecraft Pose Estimation Dataset (SPEED+)

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Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions. While computer vision in general has benefited from Machine Learning (ML), training and validating spaceborne ML models are extremely challenging due to the impracticality of acquiring a large-scale labeled dataset of images of the intended target in the space environment. In response to these challenges, the Stanford Space Rendezvous Laboratory (SLAB) had established the PosE Estimation Dataset (SPEED), which relies on synthetic images for both training and validation. These are easy to mass-produce but fail to resemble the visual features and illumination variability inherent to the target spaceborne images. In order to bridge the gap between the current practices and the intended applications in future space missions, SPEED+ was introduced.

Example lightbox images with the wireframe model of the Tango spacecraft projected based on associated labels and the provided camera properties.

Example lightbox images with the wireframe model of the Tango spacecraft projected based on associated labels and the provided camera properties.

SPEED+ is the next generation spacecraft pose estimation dataset with specific emphasis on bridging the domain gap. In addition to 60,000 synthetic images for training, SPEED+ includes 9,531 hardware-in-the-loop images of a spacecraft mockup model captured from the Testbed for Rendezvous and Optical Navigation (TRON) facility native to SLAB. TRON is a first-of-a-kind robotic testbed capable of capturing an arbitrary number of target images with accurate and maximally diverse pose labels and high-fidelity spaceborne illumination conditions. SPEED+ was used in the second international Satellite Pose Estimation Challenge co-hosted by SLAB and the Advanced Concepts Team of the European Space Agency to evaluate and compare the robustness of spaceborne machine learning models trained on synthetic images.

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Related Publications

Park, T. H., D’Amico, S.;
Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap;
11th International Workshop on Satellite Constellations & Formation Flying, Milano, Italy, June 7-10 (2022).

Park, T. H., Märtens, M., Lecuyer, G., Izzo, D., D’Amico, S.;
SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap;
IEEE Aerospace Conference, Big Sky, Montana, March 5-12 (2022).

Park, T. H., Bosse, J., D’Amico, S.;
Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Cases;
2021 AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Virtual, August 9-11 (2021).

Park, T. H., Märtens, M., Lecuyer, G., Izzo, D., D’Amico, S.;
Next Generation Spacecraft Pose Estimation Dataset (SPEED+) ;
Stanford Digital Repository (2021). DOI: https://doi.org/10.25740/wv398fc4383.

Kisantal M., Sharma S., Park T. H., Izzo D., Märtens M., D'Amico S.;
Satellite Pose Estimation Challenge: Dataset, Competition Design and Results;
IEEE Transactions on Aerospace and Electronic Systems, Vol. 56, No. 5, pp. 4083-4098 (2020). DOI: 10.1109/TAES.2020.2989063

Park T. H., D'Amico S.;
Generative Model for Spacecraft Image Synthesis using Limited Dataset;
2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Sharma S., Park T. H., D'Amico S.;
Spacecraft Pose Estimation Dataset (SPEED);
Stanford Digital Repository (2020).

Sharma S., D'Amico S.;
Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous;
IEEE Transactions on Aerospace and Electronic Systems (2020). DOI: 10.1109/TAES.2020.2999148.

Park T. H., Sharma S., D'Amico S.;
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft;
2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine, August 11 - 15 (2019).

Park T. H., D'Amico S.;
ESA Pose Estimation Challenge 2019;
Technical Note, Stanford Space Rendezvous Lab (SLAB), July 3 (2019).

Sharma S., D'Amico S.;
Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Neural Networks;
29th AAS/AIAA Space Flight Mechanics Meeting, Ka'anapali, Maui, HI, January 13-17 (2019).

Sharma S.;
Pose Estimation of Uncooperative Spacecraft using Monocular Vision and Deep Learning;
Stanford University, PhD Thesis (2019).

Sharma S., Ventura, J., D’Amico S.;
Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous;
Journal of Spacecraft and Rockets (2018).

Sharma S., Beierle C., D’Amico S.;
Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks;
IEEE Aerospace Conference, Yellowstone Conference Center, Big Sky, Montana, March 3-10 (2018).

Sharma S., Beierle C., D'Amico S.;
Generative Adversarial Networks for High-Fidelity Simulation of Spacecraft Proximity Operations;
Technical Note, Stanford Space Rendezvous Lab (SLAB), April 23 (2018).

Sharma S., Beierle C., D’Amico S.;
Towards Pose Determination for Non-Cooperative Spacecraft Rendezvous using Convolutional Neural Networks;
International Conference on Space Situational Awareness (ICSSA), Orlando, Florida, November 13-15 (2017).

Sharma S., D’Amico S.;
Reduced-Dynamics Pose Estimation for Non-Cooperative Spacecraft Rendezvous using Monocular Vision;
40th Annual AAS Guidance and Control Conference, Breckenridge, Colorado, February 2-8, 2017.

Sharma S., D’Amico S.;
Comparative Assessment of Techniques for Initial Pose Estimation using Monocular Vision;
Acta Astronautica, 123 pp. 435-445 (2016).
DOI: 10.1016/j.actaastro.2015.12.032

Sharma S., Koenig A., Sullivan J., D'Amico S.;
Verification of Light-box Devices for Earth Albedo Simulation;
Technical Note, Stanford Space Rendezvous Lab (SLAB), January (2016).

Sharma S., D’Amico S.;
Comparative Assessment of Techniques for Initial Pose Estimation using Monocular Vision;
8th International Workshop on Satellite Constellations and Formation Flying, IWSCFF 2015, 8-10 June, Delft University of Technology (2015).