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RoadRunner - Learning Traversability Estimation for Autonomous Off-road Driving
Furthermore, RoadRunner improves the system latency by a factor of roughly 4, from 500 ms to 140 ms, while improving the accuracy for traversability costs and elevation map predictions.
ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation
This GITL operation limits the distance that can be driven in a day to a few hundred meters, which is the distance that the rover can maintain acceptable localization error via relative methods.
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain
Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert.
Lunar Rover Localization Using Craters as Landmarks
At the end of each drive, a ground-in-the-loop (GITL) interaction is used to get a position update from human operators in a more global reference frame, by matching images or local maps from onboard the rover to orbital reconnaissance images or maps of a large region around the rover's current position.
