## 3 Indoor Experiment
### Indoor Experimental Results  
Figure 6 shows the autonomous driving robot conducting the location estimation experiment at the indoor laboratory. It was observed that the autonomous driving robot stably drove along the central line of the road in the entire experimental section; the ability to climb on a temporarily made ramp was used to measure the accuracy of the z-axis. The autonomous driving robot was driven on approximately the same path during the five repeated experiments, taking an average of 73.3 seconds to drive through the entire indoor laboratory. Figure 7 shows the robot's actual driving path, and the pathways measured by the three location estimation methods. Overall, the IMU + LiDAR + encoder and IMU + encoder sensors showed similar driving paths, with the IMU + LiDAR + encoder sensors showing higher accuracy than the IMU + encoder and LiDAR + encoder sensors. The mean absolute error of the IMU + LiDAR + encoder  
Figure 6: Photos showing indoor experimental scenes of the autonomous driving robot (**a**) at the starting point, (**b**) X change section, (**c**) X-Y change section, (**d**) X-Z change section, (**e**) Y change section, and (**f**) the end point. sensors was 0.09 and 0.08 m in the X and Y directions, respectively. For the IMU + encoder sensors, the mean absolute error was 0.20 and 0.18 m in the X and Y directions, respectively. Additionally, for the LiDAR + encoder sensors, the mean absolute error was 0.90 and 0.72 m in the X and Y directions, respectively. The LiDAR + encoder sensors produced greater errors in the sections where the robot's orientation angle changes rapidly, and the accuracy of the location estimation decreases significantly in the sections where the robot rotates vertically. However, the IMU + LiDAR + encoder and IMU + encoder methods show a flow that is similar to the actual driving path. The encoder sensors were used to measure the distances in all methods, and the IMU and LiDAR sensors were used to measure the robot's heading angle. Therefore, the difference in the driving path was caused by the accuracy difference between the two heading measurement sensors. When comparing the two types of heading measurement sensors, the cumulative error was expected to be relatively large for the LiDAR sensors as there was no filter to correct the raw value. It was also expected that there would be a difference between the angle at which the robot would be set to drive along the central point of the road and the angle at which the robot would actually drive. In addition, if the robot rotates at a large angle on the road, it does not recognize the accurate angle. Based on the results, the IMU + LiDAR + encoder sensors showed an overall higher accuracy than the IMU + encoder and LiDAR + encoder sensors. For the IMU sensors, three types of sensors (acceleration, angular velocity, and magnetic) are fused and calibrated in real time to estimate the robot's angle and can recognize directional rotation of up to 180\({}^{\circ}\). In contrast, the LiDAR sensors measure the distance to the left and right walls and estimate the heading direction according to the difference between the two. Therefore, when the distance to the left and right wall suddenly changes, the calculated heading value also tends to vary significantly. In particular, it was confirmed that the direction estimation ability is rapidly decreasing at a 90\({}^{\circ}\) intersection. Therefore, if the two types of heading measurement sensors were compared, the accuracy of the IMU sensor is higher than that of the LiDAR sensor, but the IMU + LiDAR + encoder sensors combined, leveraging the advantages of each of the two sensors according to the heading angle, which showed the highest accuracy overall. Figure 7: Autonomous robot’s driving path estimated by the three location estimation methods (inertial measurement unit (IMU) + encoder sensors, light detecting and ranging (LiDAR) + encoder sensors, and IMU + LiDAR + encoder sensors) and the actual driving path in the indoor experiment.