## 1 Introduction  
When autonomous mobile platforms such as autonomous vehicles and mobile robots navigate external environments, the difference in road representation between off-the-shelf maps and actual drivable conditions can lead to potential confusion and accidents.These incidents include scenarios where robotic agents descend stairs, fall off cliffs, or navigate into unexpected construction sites. Therefore, real-time estimates of drivable regions (surfaces where a mobile robot can travel, such as roads and sidewalks) are crucial for safe and reliable autonomous navigation systems. Drivable region mapping has two key requirements. The first is simultaneous localization and mapping (SLAM) [1, 2, 3], which allows agents to determine their pose and the structure of the surrounding environment. The second is semantic segmentation [4, 5, 6], which involves understanding what constitutes the surrounding environment. Based on these two technologies, semantic mapping not only maps 3D space but also its meaning. The most basic map representation method for expressing 3D space is to use point clouds, and, thus, the semantic point cloud map contains a semantic label for each point. When considering mapping a larger area, two key factors must be considered for large-scale semantic mapping. First, it is essential to create accurate onsite maps in real time. Second, multiple onsite maps must be effectively merged through postprocessing. For the first key factor, the most important component in creating accurate onsite maps is loop closure detection. Loop closure refers to the identification of a loop when a specific building or area is circumnavigated and the agent returns to the visited area. When a loop is detected, a globally matched edge is created. This step adjusts the entire recorded trajectory to reduce errors, following the principles of global matching. In addition, the map is transformed according to the trajectory changes. Unsuccessful loop creation or a map in need of modification potentially hinders the collection of large volumes of data for map construction. Moreover, the absence of semantic information, such as roads or sidewalks, can lead to inaccurate path planning. Thus, studies on semantic SLAM [7; 8; 9] are also being conducted to address this issue. Semantic SLAM extracts semantic information from images and uses it within SLAM. However, in these methods, loop closure is either not applied, or the segmented information is not refined after loop closure. For the second key factor, when dealing with large-scale tasks, an additional mapping process is needed. Map-merging combines two or more submaps into a single global map [10; 11; 12; 13; 14; 15; 16]. Using partially constructed maps to generate a large-scale map is highly efficient. Map-merging methods vary significantly depending on the type of maps, such as grid, topological, feature-based, or point cloud maps. This research adopts a point cloud registration method [17; 18; 19]. In map-merging via these registration methods, there is a risk of failure in complex environments because it relies solely on the geometric information of the whole point cloud. Additionally, it requires using all the points within the submaps, resulting in higher computational costs. To resolve the two aforementioned issues, our proposed method considers two stages. The first stage is the onsite drivable region mapping stage, and the second stage is the mapping stage. In the first stage, drivable region maps are created in real time. Additionally, the map-merging stage allows us to combine multiple segmented drivable region maps into a large-scale map. The main goal of the first stage is to estimate drivable regions using both visual SLAM and semantic segmentation. Our approach addresses the misalignment issue arising from semantic SLAM. The loop closure refinement is implemented by considering the semantic label encoded error function.