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

2026-03-17

AI Summary

This work proposes a modular, full-stack autonomous navigation system tailored for lunar surface operations, where GNSS is unavailable and visual conditions are severely degraded. The system integrates lightweight semantic segmentation with stereo visual odometry and employs a factor-graph-based SLAM backend incorporating loop closure detection to achieve globally consistent, high-precision localization. A hierarchical planning architecture is designed, in which high-level path planning encourages loop closures and systematic area coverage, while local navigation leverages arc-based sampling combined with geometric obstacle detection for real-time collision avoidance. Evaluated in high-fidelity lunar simulations, the system demonstrates centimeter-level localization accuracy, high-quality mapping, and strong repeatability, ultimately securing first place in the Lunar Autonomy Challenge finals.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• lunar autonomy
• GNSS-denied navigation
• visual SLAM
• autonomous mapping
• motion planning

Innovation

• semantic segmentation
• visual odometry
• pose graph SLAM
• loop closure
• arc sampling