为登月挑战开发全栈自主导航系统，实现厘米级定位与高精度地图构建。
Full Stack Navigation, Mapping, and Planning for the Lunar Autonomy Challenge
- 模块化设计融合感知、定位、建图与规划，支持视觉挑战环境下的实时运行。
- 在模拟登月环境中实现厘米级定位精度和高保真地图生成，重复性优异。
- 适合机器人自主探索、深空探测等复杂环境任务，技术可复用于其他场景。
我们为登月自主挑战赛开发了一套模块化的全栈自主系统，用于月球表面的导航与建图。系统在无卫星导航、视觉条件恶劣的环境中运行，整合了语义分割、双目视觉里程计、带回环检测的位姿图SLAM以及分层规划与控制。采用轻量级学习型感知模型实现实时分割与特征追踪，并通过因子图后端保持全局一致的定位。高层路径规划旨在提升地图覆盖度并促进频繁回环闭合；局部运动规划采用弧线采样结合几何障碍物检测，实现高效响应式控制。我们在竞赛的高保真月球模拟器中评估该方法，结果表明系统具备厘米级定位精度、高保真地图生成能力，并在不同随机种子和岩石分布下表现出强重复性。最终方案在决赛评估中获得第一名。
原文摘要 · Abstract (English)
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.
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