## 2 Materials and Methods
### Machine Vision Algorithm  
In this study, a machine vision algorithm was used to recognize road signs in underground mines. In the case of general roads, an object recognition technology based on artificial intelligence using a large amount of learning data should be used to recognize a wide variety of road signs. In contrast, in the case of underground mines, it is sufficient to recognize only the right and left road signs at intersections because the driving route is limited to the excavated tunnel. Therefore, in this study, we used a geometric matching algorithm, which is a machine vision technology that uses a single image as learning data to recognize road signs without the use of several computational resources. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Model** & **Specification** \\ \hline Main Controller & \begin{tabular}{c} Laptop PC \\ Windows 10 (Microsoft Corporation, \\ Redmond, WA, USA) \\ \end{tabular} & \begin{tabular}{c} Intel Core i7-9750H CPU 4.50 GHz (Intel, Santa Clara, \\ CA, UAS), 16 GB RAM, NVIDIA GeForce 1650 4 GB \\ (NVIDIA, Santa Clara, CA, USA) \\ \end{tabular} & \begin{tabular}{c} Size: 650 mm (length) \(\times\) 470 mm (width) \(\times\) 158 mm (height) \\ \end{tabular} \\ \multirow{4}{*}{Mobile Robot} & \begin{tabular}{c} Weight: 8 kg \\ Drive: 4 different gears \\ Max speed: 8 km/h \\ Max run time: 2.5 h \\ Battery: 14.8 V, 12 ah \\ Lighting intensity: 1050 lumen \\ \end{tabular} \\ \hline Vision Camera & \begin{tabular}{c} Bumblebee XB3 (Filar, Wilsonville, OR, USA) \\ \end{tabular} & \begin{tabular}{c} Image: ICCX445 (1280 \(\times\) 960 max pixels) \\ 3.75 nm square pixels \\ Interface: IEEE-1394 \\ Frame Rate: 16 FPS \\ \end{tabular} \\ \hline LiDAR & \begin{tabular}{c} LMS-111 (SICK, Waldkirch, Germany) \\ \end{tabular} & \begin{tabular}{c} Field of View: 270\({}^{\circ}\) \\ Interface: TCP/IP \\ Operating Range: 0.5 m\(-\)20 m \\ Scanning frequency: 25 Hz/50 Hz \\ \end{tabular} \\ \hline IMU & \begin{tabular}{c} EBIMU–9 DOFV4 (E2 BOX, Hanam, Korea) \\ \end{tabular} & \begin{tabular}{c} Error: Roll/Pitch \(\pm\) 0.2\({}^{\circ}\), Yaw \(\pm\) 0.5\({}^{\circ}\) \\ Output Range: \(-\)180\(-\)180\({}^{\circ}\) \\ \end{tabular} \\ \hline \begin{tabular}{c} Wheel encoder \\ \end{tabular} & \
\begin{tabular}{c} IG-32 PGM 01 TYPE (YOUNCJIN B\&B, Seoul, \\ Korea) \\ \end{tabular} &
\begin{tabular}{c} Motor gear ratio: 13 Encoder gear ratio: 61 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of sensors, controller, and driving platform used in this study. Figure 1: Autonomous driving robot and sensors used in this study: (**a**) external view, and (**b**) internal view. Geometric matching is a technology that detects the boundary line of an object using an edge detection algorithm, compares it with the shape of a template image, and matches it. Geometric matching algorithms can be used efficiently when the distinction between the object and the background is clear; however, the efficiency is low when the boundary of the object is not clear or when matching only a part of the object. Geometry matching shows high performance even in the presence of lighting changes, blurring, and noise. It can be efficiently performed based on geometrical shape changes, such as the movement, rotation, and scale change of an object on a screen. Geometric matching can be classified into commonly used edge-based geometric matching techniques and feature-based geometric matching techniques for matching circular, square, and linear template images. The geometric matching algorithm consists of the following steps: learning (curve extraction and feature extraction) and matching (feature correspondence matching, template model matching, and match refinement). Figure 2 shows the template image and matching result of geometric matching. At the top of the matching result image, the matching image number, the center pixel coordinates of the matched image, and the matching score are displayed.