3D Shape Reconstruction of Small Bodies From Sparse Features

Jarvis, Benjamin W.; Choi, Gilwoo; Hockman, Benjamin; Morrell, Benjamin; Bandopadhyay, Saptarshi; Lubey, Daniel P.; Villa, Jacopo; Bhaskaran, Shyam; Bayard, David S.; Nesnas, Issa

doi:10.1109/lra.2021.3097273

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…These features, however, could enable important autonomous on-board capabilities. While a variety of algorithms have been developed to detect, match, and track image's features generated by morphological properties for optical navigation and shape reconstruction purposes around a small-body [1,2], fewer methods have been developed to discern between the different classes these morphological features belong to. Semantic segmentation can be defined [3] as the capability to perform both object recognition and accurate boundary segmentation at pixel level.…”

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confidence: 99%

Small-Body Segmentation Based on Morphological Features with a U-Shaped Network Architecture

Pugliatti

Maestrini

2022

Journal of Spacecraft and Rockets

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Small-bodies such as asteroids and comets display great variability in terms of surface morphological features. These are often unknown beforehand but can be employed for hazard avoidance during landing, autonomous planning of scientific observations, and navigation purposes. Algorithms performing these tasks are often data-driven, which means they require realistic, sizeable, and annotated datasets which in turn may rely heavily on human intervention. This work develops a methodology to generate synthetic, automatically-labeled datasets which are used in conjunction with real, manually-labeled ones to train deep-learning architectures

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confidence: 99%