Carol Martínez — accepted AI-conference papers

2022

Enhancing Rover Teleoperation on the Moon With Proprioceptive Sensors and Machine Learning Techniques

Sofía Coloma, Carol Martínez, Baris Can Yalçin, Miguel A. Olivares-Méndez

RA-L 2022

Geological formations, environmental conditions, and soil mechanics frequently generate undesired effects on rovers' mobility, such as slippage or sinkage. Underestimating these undesired effects may compromise the rovers' operation and lead to a premature end of the mission. Minimizing mobility ris

Cited by 16 Source ↗Scholar ↗

2021

Enhancing Lunar Reconnaissance Orbiter Images via Multi-Frame Super Resolution for Future Robotic Space Missions

Jose Ignacio Delgado-Centeno, Pedro J. Sanchez-Cuevas, Carol Martínez, Miguel A. Olivares-Méndez

RA-L 2021

This paper presents a novel application of a Multi-frame Super Resolution (MFSR) method for lunar surface imagery called Lunar HighRes-net (L-HRN). In this work, we adapted and used NASA's Lunar Reconnaissance Orbiter (LRO) image database to train the Deep Learning architecture for image super resol

Cited by 12 Source ↗Scholar ↗