Full Stack Navigation, Mapping, and Planning for the Lunar Autonomy Challenge

Adam Dai, Asta Wu, Keidai Iiyama, Guillem Casadesus Vila, Kaila Coimbra, Thomas Deng, and Grace Gao

Peer Reviewed

Abstract:	We present a modular, full-stack autonomy system for lunar surface navigation and mapping developed for the Lunar Autonomy Challenge. Operating in a GNSS-denied, visually challenging environment, our pipeline integrates semantic segmentation, stereo visual odometry, pose graph SLAM with loop closures, and layered planning and control. We leverage lightweight learning-based perception models for real-time segmentation and feature tracking, and use a factor-graph backend to maintain globally consistent localization. High-level waypoint planning is designed to promote mapping coverage while encouraging frequent loop closures, and local motion planning uses arc sampling with geometric obstacle checks for efficient, reactive control. We evaluate our approach in the competition’s high-fidelity lunar simulator, demonstrating centimeter-level localization accuracy, high-fidelity map generation, and strong repeatability across random seeds and rock distributions. Our solution achieved first place in the final competition evaluation.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	1986 - 2002
Cite this article:	Dai, Adam, Wu, Asta, Iiyama, Keidai, Vila, Guillem Casadesus, Coimbra, Kaila, Deng, Thomas, Gao, Grace, "Full Stack Navigation, Mapping, and Planning for the Lunar Autonomy Challenge," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 1986-2002. https://doi.org/10.33012/2025.20447
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