LunarLoc: Segment-Based Global Localization on the Moon

Annika Thomas, Robaire Galliath, Aleksander T Garbuz, Luke R Anger, Cormac ONeill, Trevor E Johst, Dami V Thomas, George Lordos, JONATHAN P HOW

Keywords: Global Localization, Lunar, Autonomy, Robotics

TL;DR: LunarLoc uses open-set segmentation to generate maps through detection of boulders, then performs object-based global localization using multi-session maps.

Abstract: Global localization is necessary for autonomous operations on the lunar surface where traditional Earth-based navigation infrastructure, such as GPS, is unavailable. As NASA advances toward sustained lunar presence under the Artemis program, autonomous operations will be an essential component of tasks such as robotic exploration and infrastructure deployment. Tasks such as excavation and transport of regolith require precise pose estimation, but proposed approaches such as visual-intertial odometry (VIO) accumulate odometry drift over longer traverses. Precise pose estimation is particularly important for upcoming missions such as the ISRU Pilot Excavator (IPeX) that rely on autonomous agents to operate over extended timescales and varied terrain. To help overcome odometry drift over longer traverses, we propose LunarLoc, an approach to global localization that leverages instance segmentation for zero-shot extraction of boulder landmarks from onboard stereo imagery. Segment detections are used to construct a graph-based representation of the terrain, which is then matched to a reference map of the environment captured during a previous session using graph-theoretic data association. This method enables accurate and drift-free global localization in visually ambiguous settings. LunarLoc achieves sub-cm level accuracy in multi-session global localization, significantly outperforming the state of the art in lunar global localization. To encourage the development of further methods for global localization on the Moon, we will release our datasets publicly with a playback module upon acceptance.

Submission Number: 15