RT Conference Proceedings
T1 Hardware-Accelerated Mars Sample Localization Via Deep Transfer Learning From Photorealistic Simulations.
A1 Castilla-Arquillo, Raúl
A1 Pérez-del-Pulgar-Mancebo, Carlos Jesús
A1 Paz-Delgado, Gonzalo Jesús
A1 Gerdes, Levin
K1 Robots autónomos
AB 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.
YR 2023
FD 2023-05
LK https://hdl.handle.net/10630/27083
UL https://hdl.handle.net/10630/27083
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026