## 4 Experiments and Results
### Map-Merging Results  
For map-merging, the process described in Figure 4 was sequentially applied to the data captured in Figure 8a-h, resulting in Figure 10c. For comparison, map-merging was also performed using the trajectory provided by ORB-SLAM3, yielding Figure 10b. The frame of the most visually similar image in each map was manually identified. The quaternion values specified in the trajectory were then used to generate the transformation matrix for each map, which was applied to the world coordinate system. Figure 10a represents the best result obtained by visually comparing and aligning the point clouds of each map for the optimal merging outcome. A comparison of Figure 10a,b reveals that Figure 10b has significant errors. Discrepancies are particularly noticeable in the central circular section. By contrast, Figure 10c demonstrates precise alignment, indicating the successful execution of our proposed method. In Figure 11a,b, the increase in data distorted the overall map, even in regions where the original loop closure performed well. Additionally, despite using the same dataset and settings, large-scale map generation consistently produces different results. By contrast, Figure 11c is the result of data captured in separate regions to create maps, avoiding issues during map creation with multiple loops. This approach produced more stable results during large-scale mapping. In Figure 12a,b, the map and trajectory of the provided ground truth are shown. Figure 12b shows the trajectories of maps 1, 2, and 3, which are displayed in the same way as those in Figure 6. As shown in Figure 12c,d, ORB-SLAM3 failed to localize the camera pose. Therefore, the mapping result also failed even though we reran the SLAM after the initialization process. Figure 12e,f shows the results of the proposed method. Compared with the ORB-SLAM3 result, our proposed method produces better results. It is also evident that, in a large-scale area, dividing the map region and generating a map within those areas yields better results. These findings are summarized in Table 2.  
As shown in Figures 11a,b and 12c,d, the results of existing methods that perform mapping without dividing the path do not consistently yield the same outcomes because of drift error, resulting in failures in achieving proper mapping. However, as shown in Figures 11c and 12e, the results obtained via the proposed method demonstrate significant improvement. The proposed method successfully performs drivable region mapping, as demonstrated in Figures 11c and 12e. It accurately detects roads where vehicles can travel, enabling the creation of maps at a large scale. Figure 9: Comparison results of the Urban 28 dataset [55] using the proposed method with the provided ground truth trajectory. The trajectory of each map is manually divided, as shown in Figure 6. The comparison was conducted using the EVO [61]. The dashed line represents the ground-truth trajectory of Urban Region 28. (**a**) Map 1 drivable region mapping results. (**b**) Map 1 trajectory-comparison image. (**c**) Map 3 drivable region mapping results. (**d**) Map 3 trajectory-comparison image. (**e**) Map 2 drivable region mapping results. (**f**) Map 2 trajectory-comparison image. Moreover, to the best of our knowledge, this study represents the first instance of large-scale drivable region mapping using only a camera. Figure 10: Map-merging results of Jeonbuk National University. A comparison of map-merging using three different methods is shown in the accompanying image. By zooming in on a portion of the map, we compare the quality on the quality on the degree of overlap in the drivable regions. The more accurately the central circles overlap, the better the result. (**a**) Map-merging by manually transforming the positions of the eight maps. (**b**) Map-merging using image similarity and the poses of the eight maps. (**c**) Map-merging via the proposed method.