Hardware-Accelerated Mars Sample Localization Via Deep Transfer Learning From Photorealistic Simulations

The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected...

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Vydané v:IEEE robotics and automation letters Ročník 7; číslo 4; s. 12555 - 12561
Hlavní autori: Castilla-Arquillo, Raul, Perez-del-Pulgar, Carlos, Paz-Delgado, Gonzalo Jesus, Gerdes, Levin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Computer architecture
Computer vision
Datasets
Deep learning for visual perception
Electron tubes
Feature extraction
Hardware
hardware-software integration in robotics
Laboratory tests
Localization
Location awareness
Machine learning
Mars
Mars rovers
Mars sample return missions
Mars surface
Pose estimation
RGB-D perception
space robotics and automation
Test stands
Transfer learning
Tubes
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí:The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3219306