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ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness
The ability to determine the pose of a rover in an inertial frame autonomously is a crucial capability necessary for the next generation of surface rover missions on other planetary bodies.
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images
In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset.
Mixed-domain Training Improves Multi-Mission Terrain Segmentation
Planetary rover missions must utilize machine learning-based perception to continue extra-terrestrial exploration with little to no human presence.
Mars Terrain Segmentation with Less Labels
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection.
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning
A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network's capacity across its 12 antennae.
