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Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy

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Pubblicato in:arXiv.org (Jan 23, 2024), p. n/a
Autore principale: Ramchander Bhaskara
Altri autori: Georgakis, Georgios, Nash, Jeremy, Cameron, Marissa, Bowkett, Joseph, Ansar, Adnan, Majji, Manoranjan, Backes, Paul
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Datasets
Space missions
Matching
Visual perception
Icy satellites
Photometry
Hypotheses
Lunar surface
Jupiter
Europa
Algorithms
Machine learning
Enceladus
Simulation
Terrain
Visual perception driven algorithms
Sampling
Autonomy
Accesso online:Citation/Abstract
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Abstract:Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss.
ISSN:2331-8422
DOI:10.1109/AERO58975.2024.10521439
Fonte:Engineering Database