## 3 Indoor Experiment
### Indoor Experimental Results
Table 2 shows the location estimation methods' mean absolute error for each section during the five experiments. The X-change section has a straight portion with a constant road width. In this straight section, the distance from the LiDAR sensor to the left and right walls is similar; therefore, the location estimation accuracy was excellent when using the high-performance LiDAR sensor. On the other hand, the IMU sensors showed relatively high errors; it was inferred that they were caused by the robot's body vibration during driving, and the poor performance of the magnetic sensor when it was close to metallic materials. The IMU + LiDAR + encoder and LiDAR + encoder sensors showed the same results because the headings were measured in the same way through the LiDAR sensors in the X-change section. In the X-Y change section, the IMU + LiDAR + encoder and IMU + LiDAR sensors had cumulative errors similar to the previous section, whereas the LiDAR sensor's errors increased sharply as the road width changed and the steering shift increased. In the X-Z change section, the IMU + encoder sensors showed a similar cumulative error to the previous section; however, the LiDAR + encoder sensors had fewer cumulative errors compared to the previous sections. There was no shift in steering because the height in the Z direction changed, but the Y value did not. Therefore, it was observed that the error in that section was relatively small, similar to the first section, and since the IMU + LiDAR + encoder sensors measure heading angle using the LiDAR sensor in a straight section, errors were accumulated in a similar manner to the LiDAR + encoder sensors. Finally, in the Y-change section, the LiDAR + encoder sensors showed the largest error among all sections, whereas the IMU + encoder sensors' accumulated errors remained constant, as in all the previous sections. The LiDAR sensor could not detect the driving direction of the robot whenever it used a large steering change to turn, hence the X and Y values showed large errors. Overall, all location estimation methods tended to accumulate errors over time. The IMU + encoder sensors showed similar errors in each section, whereas the LiDAR sensors showed very high accuracy in the straight sections, but their location estimation accuracy decreased sharply in the sections where there was a large steering shift. The IMU + LiDAR + encoder sensor generated errors in each section corresponding to the method with the highest accuracy in that section among the two different methods, so the total accumulated error was the smallest. Figure 8 shows the X-Z position of the autonomous driving robot measured on an inclined terrain. The length and height of the Z-axis terrain are approximately 1.7 and 0.125 m, respectively. The tilt angle of the terrain was measured by the pitch angle estimated by the IMU sensor, and the distance was measured by the encoder sensor. The mean absolute error obtained from comparing the 58 measured Z-coordinates and the actual slopes was 0.58 cm. In Figure 9, the lateral and longitudinal absolute error and velocity between the three types of location estimation methods (IMU + encoder, LiDAR + encoder, and IMU + LiDAR + encoder sensors) were compared. When measuring the robot's heading angle, it was found that combining the two sensor types generated a smaller error than that of the two previously used location estimation methods. These results show a significant difference in the overall location estimation accuracy, as there