## 5 Conclusions  
This study proposed a method for mapping large-scale drivable regions that uses semantic segmentation, visual simultaneous localization and mapping, and map-merging. Semantic visual simultaneous localization and mapping identifies regions where robots can safely navigate, using semantic segmentation and visual localization to create maps that ensure safe movement. The created maps are merged into a single large-scale map using our proposed map-merging method, which uses 3D point cloud registration. Figure 12: Urban 28 dataset [55] drivable region mapping by two methods (single run without splitting data; proposed method) results. (**a**) The provided ground-truth point cloud map of Urban 28. (**b**) The provided ground-truth trajectory of Urban 28. Blue, red, and green represent the trajectories of maps 1, 2, and 3, respectively. (**c**) Result of the first run of drivable region mapping without splitting the data. The resulting map shows misalignment. (**d**) Result of the second run of drivable region mapping without splitting the data. The resulting map shows misalignment. (**e**) A drivable region map produced by dividing the map and merging it with the proposed method. (**f**) Comparison between the trajectory generated via the proposed method and the ground-truth trajectory via EVO [61]. The dashed line represents the ground-truth trajectory of Urban 28. The semantic segmentation and visual simultaneous localization and mapping used for real-time drivable region mapping demonstrated adequate real-time performance, enabling the creation of drivable region maps in real-world scenarios. Semantic segmentation and visual simultaneous localization and mapping were integrated through pose transformation and loop closure. In the process of map-merging to create large-scale maps, a method that uses 3D point cloud registration based on the feature descriptors of overlapping regions' 3D point clouds was proposed. This method yielded positive results, particularly with respect to the drift error of visual simultaneous localization and mapping, which tends to increase as the number of images increases. Moreover, our method demonstrated good results compared with real satellite maps. The proposed method successfully generated a large-scale map of drivable regions. Our method was verified with both our large-scale custom dataset and publicly available benchmark dataset. Furthermore, the proposed map-merging method mitigated drift errors during simultaneous localization and mapping (SLAM) in large-scale environments. On average, only 19% of the data--the ratio of images that correspond to the overlapping area among the entire images--was used for map-merging. Despite utilizing a relatively small amount of data, the process was highly effective. These findings highlight the efficiency and accuracy of our approach to drivable region mapping, a critical component for path planning in autonomous driving systems. Our future work will focus on visual localization and map updates on large-scale drivable maps composed of 3D point clouds. Additionally, we aim to explore visual-based large-scale drivable mapping in areas with limited features, such as rural environments, as opposed to university campus settings. Conceptualization, C.H. and D.Y. ; methodology, C.H. and D.Y. ; software, C.H. and D.Y. ; validation, C.H. ; formal analysis, C.H. ; investigation, C.H. and D.Y. ; resources, G.W. and S.C.K. ; data curation, C.H. ; writing--original draft preparation, C.H. and D.Y. ; writing--review and editing, C.H. and H.J. ; funding acquisition, H.J.