无需GPU和网络，在矿洞中实现四足机器人全自主导航
Efficient Autonomous Navigation of a Quadruped Robot in Underground Mines on Edge Hardware
- 基于边缘计算的纯本地化导航系统，全程无依赖
- 700米自主行进成功率达100%，路径成功率0.73±0.09
- 适合资源受限的地下复杂环境，无需训练或调参
地下矿洞中的实体导航面临诸多挑战：狭窄通道、不平地形、近乎全黑、无GPS信号及通信基础设施薄弱。现有学习方法依赖GPU加速推理与大量训练数据，本文提出一套完全自主的导航系统，运行于低功耗Intel NUC边缘计算机（无GPU、无网络连接）。系统集成激光雷达-惯性里程计、基于先验地图的扫描匹配定位、地形分割与可见图全局规划，并配合速度调节的局部路径跟踪器，实现感知到动作的实时闭环控制。仅需一次环境建图，即可在已知地图内自由到达任意目标点，无需环境特异性训练或学习模块。通过四组不同难度目标的实地测试（共20次重复，每目标5次），累计完成700米以上全自主行进，成功率达100%，整体路径成功率（SPL）为0.73±0.09。
原文摘要 · Abstract (English)
Embodied navigation in underground mines faces significant challenges, including narrow passages, uneven terrain, near-total darkness, GPS-denied conditions, and limited communication infrastructure. While recent learning-based approaches rely on GPU-accelerated inference and extensive training data, we present a fully autonomous navigation stack for a Boston Dynamics Spot quadruped robot that runs entirely on a low-power Intel NUC edge computer with no GPU and no network connectivity requirements. The system integrates LiDAR-inertial odometry, scan-matching localization against a prior map, terrain segmentation, and visibility-graph global planning with a velocity-regulated local path follower, achieving real-time perception-to-action at consistent control rates. After a single mapping pass of the environment, the system handles arbitrary goal locations within the known map without any environment-specific training or learned components. We validate the system through repeated field trials using four target locations of varying traversal difficulty in an experimental underground mine, accumulating over 700 m of fully autonomous traverse with a 100% success rate across all 20 trials (5 repetitions x 4 targets) and an overall Success weighted by Path Length (SPL) of 0.73 \pm 0.09.
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