# LunarLoc: Segment-Based Global Localization on the Moon

arXiv: https://arxiv.org/abs/2506.16940 · HTML: https://mlsanity.com/paper/2506.16940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [CLIPPER: A Graph-Theoretic Framework for Robust Data Association](https://mlsanity.com/paper/2011.10202/) (2020)
- [ROMAN: Open-Set Object Map Alignment for Robust View-Invariant Global Localization](https://mlsanity.com/paper/2410.08262/) (2024)
- [LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigation](https://mlsanity.com/paper/2301.01350/) (2023)
- [Global Localization in Unstructured Environments using Semantic Object Maps Built from Various Viewpoints](https://mlsanity.com/paper/2303.04658/) (2023)
- [SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments](https://mlsanity.com/paper/2401.04791/) (2024)
- [ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness](https://mlsanity.com/paper/2405.01673/) (2024)
- [Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching](https://mlsanity.com/paper/2409.11555/) (2024)

## Built on by (most cited first, 1 of 1)

- [Globally Localizing Lunar Rover in Pixels via Graph Alignment](https://mlsanity.com/paper/2606.10602/) (2026)

## Abstract (arXiv)

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-inertial odometry (VIO) accumulate odometry drift over long 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 long 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 aligned with 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 experiments, 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 release our datasets publicly with a playback module: https://github.com/mit-acl/lunarloc-data.