## 5 Conclusions  
In this study, an accuracy comparison experiment was conducted for three location estimation methods of an autonomous driving robot (IMU + encoder, LiDAR + encoder, and IMU + LiDAR + encoder sensors) in an indoor laboratory that simulated an underground mine, and an actual underground mine. The robot location was estimated by each of the three methods as the autonomous vehicle was driving through the indoor laboratory, in a total of five repetitive experiments, and through an underground mine. The results accuracy was analyzed by comparing the estimated with the actual robot location. From the results, the IMU + LiDAR + encoder sensors generally showed the highest accuracy, followed by the IMU + encoder sensors and the LiDAR + encoder sensors. When the entire test site was divided into sections, the IMU + encoder sensors showed high performance on the curved roads, whereas the LiDAR + encoder sensors showed high performance on the straight roads. The IMU sensor showed a constant error in all the sections owing to the robot's vibration, whereas the LiDAR sensor showed relatively high accuracy in the straight sections with a constant distance to the wall. Since the IMU + LiDAR + encoder sensors were used by switching between the two types  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **IMU + Encoder** & **LiDAR + Encoder** & **IMU + LiDAR + Encoder** \\ \hline X MAE (m) & 0.12 & 0.52 & 0.11 \\ \hline Y MAE (m) & 0.23 & 0.91 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Field experiment results of the autonomous robot’s location estimation.  
of heading measurement sensors, an error occurred corresponding to the method with the relative highest accuracy among the two different methods in each section. As a result, the IMU + LiDAR + encoder sensors method, which uses two types of heading measurement sensors together based on the steering angle, showed the highest overall accuracy in all sections.  
A correction filter that could improve the localization accuracy of the robot was not applied in this study; in the future, if localization correction algorithms such as the Kalman and particle filters are applied, the accuracy of the location estimation will be further improved. It will also be possible to improve the localization accuracy by utilizing pre-built LiDAR maps and point clouds measured by the LiDAR sensors. Additionally, if a wide range of environmental sensors such as vision cameras are used, it will be possible to check the overall road condition, and correct the position of the robot by grasping the structural shape. The autonomous robot used in this study drove at the same speed on all paths. However, in the future, additional experiments should be conducted to compare changes in the location estimation accuracy based on different robot speeds.  
If autonomous robots are used in underground mining environments, they can explore areas that are difficult for humans to access, and productivity can be improved by automating the equipment used in the underground mines through autonomous driving technology. Additionally, the location estimation technology can be combined with environmental sensors to automate the overall environmental monitoring of tunnels. Location estimation technology is the basis for the work of exploring underground mines, such as tunnel mapping, environmental mapping, and optimal route planning. It is expected that the results of this study will be useful reference materials for the use of autonomous driving robots in underground mine environments.  
Y.C. conceived and designed the experiments; H.K. performed the experiments; H.K. and Y.C. analyzed the data; Y.C. contributed reagents/materials/analysis tools; H.K. and Y.C. wrote the paper. Both authors have read and agreed to the published version of the manuscript.  
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1A09083947).  
The authors declare no conflict of interest.