###### Abstract
Moreover, it is impossible to determine the location in underground mine environments through GPS. Therefore, it is necessary to develop location recognition technology for the applications. Studies have been conducted in other industries to estimate the location of autonomous vehicles and mobile robots [23; 24; 25]. [PERSON] et al. [26] estimated the location of autonomous vehicles using GPS, digital maps, and camera sensors. [PERSON] et al. [27] conducted a study to estimate the location by fusing the inertial measurement unit (IMU) sensor, encoder sensor and computer vision-based distance measurement technology. [PERSON] et al. [28] developed a location estimation system that can be used indoors using camera sensors and image processing technology. [PERSON] et al. [29] developed the localization algorithm for autonomous driving robots using ultrasonic sensors in an indoor semi-structured environment and evaluated its accuracy. Additionally, in order to reduce the uncertainty that occurs when estimating the robot's location, probability localization algorithms such as the Kalman and particle filters are widely used [30; 31]. [PERSON] et al. [32] developed a localization system that combines GPS and LiDAR sensors with the Kalman filter, and which was applied to three types of process models to compare the speed and residuals in the longitudinal and lateral directions. Moreover, [PERSON] et al. [33] developed a high-speed trace car location system using the Monte Carlo localization method, which is based on the Robot Operating System (ROS), an open-source operating system for mobile robots, and the LiDAR sensor and Kalman filter. [PERSON] et al. [34] performed the localization of robots in a semi-constructed outdoor environment by utilizing a localization algorithm based on a particle filter. Consequently, this made it possible to perform location estimation quickly and easily in an indoor environment. In the mining industry, studies have been conducted to estimate the location of the autonomous driving robot. [PERSON] et al. [35] predicted the robot's attitude using an IMU sensor and fused it with an encoder sensor to estimate the robot's location. [PERSON] et al. [36] developed an autonomous driving algorithm based on the LiDAR sensor and utilized it to estimate the robot's location. Because GPS is not available in underground mine environments, and camera sensors are also limited, sensors such as IMU and LiDAR are often used to estimate the location of autonomous driving robots. However, because previous studies were focused on developing or utilizing the location estimation methods, they did not compare the accuracy of the methods in the underground mine environment. In this study, we compared the accuracy of three location estimation methods of an autonomous driving robot in an underground mine environment: inertial measurement unit with encoder (IMU + encoder) sensors, Light Detecting and Ranging with encoder (LiDAR + encoder) sensors, and IMU with LiDAR and encoder (IMU + LiDAR + encoder) sensors. The study presents the autonomous driving robot system, sensors, and location estimation methods used in the experiments. The location of the autonomous driving robot was estimated using each of the three methods as it drove through the indoor laboratory, which simulated an underground mine, and in an actual underground mine. We analyzed the accuracy of the results by comparing the estimated robot location with its actual location.