## 4 Field Experiment
### Field Experiment Results  
Figure 11 shows the autonomous robots driving in the underground mine during the field experiment. The autonomous driving robot received signals from the remote controller at the starting point and drove through four curved points stably cornered. The entire drive through the experiment section was performed along the central point of the mine, without touching the sidewalk blocks installed at a width of about 1 m, and the total driving time was approximately 66 s.  
Figure 12 shows the actual driving path of the autonomous robots in the underground mine, and the driving path measured through the three location estimation methods. Overall, the IMU + LiDAR + encoder sensors have the highest accuracy, followed by IMU + encoder sensors and the LiDAR + encoder sensors, which is similar to the indoor experiment results.  
Figure 11: Photos showing field experiment scenes of the autonomous driving robot at **(a)** the starting point, **(b)** the curved point, and **(c)** the end point.  
Figure 12: Autonomous robot’s driving path estimated by the three location estimation methods (IMU + encoder, LiDAR + encoder, and IMU + LiDAR + encoder sensors), and the actual driving path in the field experiment.  
The mean absolute error in the method using the IMU + LiDAR + encoder sensors was 0.11 m and 0.11 m in the X and Y directions, respectively (Table 3). The IMU + LiDAR + encoder sensors show similar paths to the LiDAR + encoder sensors on the straight road, and similar paths to the IMU + encoder sensors on the curved road.  
The X direction mean absolute error of the IMU + encoder sensors was 0.12 m, and the Y direction mean absolute error was 0.23 m. The IMU + encoder sensors showed a slightly higher location estimation accuracy in the field experiments compared to the indoor experiments.  
The LiDAR + encoder sensors show relatively high accuracy on straight roads, similar to the indoor experiment, while errors tended to accumulate when the robot's steering changed rapidly. The LiDAR + encoder sensors' X direction mean absolute error was 0.52 m, and the Y direction mean absolute error was 0.91 m, showing relatively high accuracy compared to the indoor experiments. In the indoor laboratory, there was a section where the width changed rapidly or was bent vertically, whereas, in the underground mines, the width of the road gradually increased and decreased, so the robot's heading did not change rapidly. Additionally, the LiDAR + encoder sensors did not recognize, in the indoor experiment, the large rotation of the robot's steering, so a large error occurred. In the field experiments, however, the robot's steering did not change rapidly because the tunnel road width increased and decreased gradually.  
In order to quantitatively compare the accuracy of the location estimation according to the steering change, the boundary between the straight and curved sections was clearly set, and the width and curvature of the road were changed significantly. However, since the underground mine tended to change gradually, in the form of shafts or road curvatures, the accuracy of the location estimation was generally increased. In particular, in the case of the LiDAR sensor, the positioning performance was very low because the sensors did not recognize the robot's large angle rotation in the indoor laboratory; however, in the underground mine environment, the robot's headings changed frequently at small angles, meaning that the accuracy of the location estimation could have increased slightly. Unlike the indoor laboratory, the actual underground mine environment is large and most of the roads are almost straight. Therefore, the importance of the LiDAR sensors, which showed high accuracy when the robot moved in a straight direction, was expected to increase further.