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

TL;DR

This work presents 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.

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.

Figures (6)

• Figure 1: (a) A third-person view of the environment and the robot during an autonomous navigation mission in an underground mine with no GPS, WiFi, or cellular signal. (b) The prior map point cloud overlaid with a real-time LiDAR scan and terrain classification (white = traversable, red = obstacle); insets show the fisheye camera view (near-dark, true lighting) and thermal image. (c) Localized map with traversed path (dashed) and robot current position; photo insets show the robot at the entrance (right), mid-journey (center), and approaching the goal (left).
• Figure 2: System architecture of the autonomous navigation stack. Dashed lines indicate offline data loaded at startup. FAST-LIO2 fuses LiDAR and IMU inputs to produce odometry and motion-undistorted scans. Two parallel branches---NDT localization (drift correction against a prior map) and terrain analysis (ground/obstacle segmentation)---feed the FAR Planner, which computes a global path tracked by a regulated pure pursuit controller.
• Figure 3: Boston Dynamics Spot with the custom navigation payload. The sensor suite includes a Velodyne VLP-16 LiDAR, a Yahboom IMU, and a TOPDON TC001 thermal camera. All processing runs onboard on an Intel NUC 13 mini PC.
• Figure 4: Goal and start locations on the mine elevation map (20 trials total). (a) Goals 1--3 share a common start near the entrance. (b) Goal 4 (Entrance): all 5 start locations marked, each from a distinct position in the mine. Dashed lines show the geodesic shortest paths used to compute the reference distance $\ell$ for SPL evaluation.
• Figure 5: Qualitative timeline and trajectory overview. Left (a--c): Goal 3 (Entrance$\rightarrow$Deep)---the robot navigates through narrow, sloped, pitch-black passages. Right (d--f): Goal 4 (Deep$\rightarrow$Entrance)---starting from a dark location, the robot returns to the entrance.