## 4 Experiments and Results
### Drivable Region Mapping Results  
PIDNet [39] has three variants based on model size: PIDNet_S, PIDNet_M, and PIDNet_L. Among these, PIDNet_L and PIDNet_M were considered unsuitable for real-time usage due to their higher number of parameters, so PIDNet_S was selected. Compared to other models, the PIDNet_S model achieves a final accuracy of 92.3% and a mean intersection over union (mIoU) of 80.2%, as shown in Table 3. According to Equation (10), accuracy is calculated by dividing the number of correctly classified pixels by the total number of pixels in the image, meaning 92.3% of the pixels were correctly classified. With a frame rate of 45.2 FPS, PIDNet_S demonstrated optimal performance for real-time applications. Although DDRNet_23 offers slightly higher accuracy and supports real-time execution, we prioritized mIoU as the key evaluation metric. For this reason, PIDNet_S was selected. In the future, the semantic segmentation model may be upgraded to a higher-performing alternative.  
\[Accuracy=\frac{Number\ of\ Correctly\ Classified\ Pixels}{Total\ Number\ of\ Pixels\ in\ the\ Image} \tag{10}\]  
The implementation results of loop closure for the drivable region mapping are shown in Figure 7. In Figure 7a, the point clouds of buildings overlapped with the drivable region, and the trajectory was not updated because of the unsuccessful loop closure of the drivable region. By contrast, Figure 7b exhibits successful loop closure of the drivable region.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Segmentation Model** & **Accuracy (\%)** & **Loss** & **mIoU (\%)** & **FPS** & **Params** \\ \hline PIDNet\_S & [39] & 92.3 & **0.111** & **80.2** & **45.2** & 7.6 M \\ \hline DeeplabV3 & [57] & 92.4 & 0.769 & 65.6 & 5.2 & 58.3 M \\ \hline PsPUnet & [58] & 90.1 & 0.312 & 75.2 & 24.9 & 46.7 M \\ \hline BiSeNetV2 & [59] & 90.7 & 0.766 & 74.8 & 33.7 & 12.8 M \\ \hline DDRNet\_23 & [60] & **96.1** & 0.212 & 73.4 & 35.7 & 20.1 M \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training results of the semantic segmentation model using the Cityscapes dataset and our custom dataset.  
Figure 6: Urban 28 dataset [55] satellite image and divided course. The course was manually divided into three sections and displayed on a satellite image. The satellite images were captured via Google Maps. The lines on the satellite images were drawn manually. The trajectories for maps 1, 2, and 3 are shown in blue, red, and green.  
In Figure 8a-h, the results of the drivable region mapping using data captured along the paths presented in Figure 5 are shown. Sequentially, they correspond to Maps 1 to 8. The cyan point clouds represent the drivable region, and the white point clouds indicate features from ORB-SLAM3.  
Figure 9 shows the result of the drivable region mapping using the Complex Urban dataset. Owing to the use of stereo images rather than RGB-D images, it was necessary to align the scale difference between the map results. Thus, alignment using evo [61] was performed, followed by performance evaluation, i.e., Root Mean Square Error (RMSE) and qualitative result evaluation. The results are shown in Table 2.  
Figure 8: Drivable region mapping results at Jeonbuk National University: (a) map1, (b) map2, (c) map3, (d) map4, (e) map5, (f) map6, (g) map7, (h) map8.  
Figure 7: (a) Drivable region mapping result without loop closure (b) Drivable region mapping result with loop closure.