Efficient Autonomous Navigation of a Quadruped Robot in Underground Mines on Edge Hardware

TL;DR

This paper presents a fully autonomous quadruped robot navigation system for underground mines that operates entirely on edge hardware without GPU or network, achieving high success in real-world trials.

Contribution

The authors develop a low-power, GPU-free autonomous navigation stack for underground mines, integrating perception, localization, and planning on a single edge computer.

Findings

100% success rate in 20 trials across 4 target locations

Over 700 meters of autonomous traversal in underground environment

Achieved real-time perception-to-action without environment-specific training

Abstract

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.