## 2 Related Work  
Visual SLAM [20; 21; 22; 23; 24; 25] is a computational method for building or updating a map of an unknown environment and estimating the current pose using visual sensors. Visual SLAM extracts visual features and optimizes poses and map points through correspondence matching by calculating geometric errors. Feature-based SLAM systems, such as VINS-MONO [24] or ORB-SLAM [25], extract key features from images, find correspondences between images, and estimate the camera pose and the 3D information of the environment. However, this process inevitably leads to drift errors over time. To address this issue, most SLAM systems employ pose graph optimization [26]-based loop closure techniques to minimize accumulated errors. Alternatively, [PERSON] et al. [27] proposed the use of satellite images as an external data source to correct drift, while [PERSON] et al. [28] utilized Wi-Fi data for error correction. Similarly, previous works [29; 30] have proposed SLAM methods that address trajectory drift by leveraging external data rather than relying on loop closure. In contrast, this study adopts an RGB-D camera-based approach with loop closure as a key component. RGB-D cameras are commonly used as visual sensors for 3D mapping because they provide more stable localization in SLAM processes and face fewer challenges with scale ambiguity compared to monocular or stereo cameras. For exploiting semantic information of the scene, semantic segmentation has been extensively studied in the field of computer vision. Prior research has investigated recognizing drivable regions from segmented images with deep learning-based methods. The results generated from the training of semantic segmentation [31; 32; 33; 34; 35] or instance segmentation [36; 37; 38], which are well-known tasks for representing semantics, can be utilized for drivable region detection. Recently, [PERSON] et al. [39] employed the semantic segmentation method to determine the real-time drivable region from an image. It applies the concept of a proportional-integral-derivative (PID) controller to a deep learning model and is designed for real-time segmentation. [PERSON] et al. [40] proposed a self-supervised learning system trained on monocular images for classification, which performs semantic mapping for roads and objects through bird's-eye-view transformation. Some studies [41; 42; 43] have introduced loop closure methods that leverage semantic information. While these approaches demonstrate the potential of semantic data in mapping and localization, they primarily focus on consecutive frame-to-frame semantic mapping. As a result, a gap between the 3D map and the semantic map appears during loop closure since the frame-to-map correspondence has not been considered. Regarding the second key factor, _map-merging process_, for large-scale drivable mapping, the descriptor of features extracted from point clouds and the registration method that uses it are important. Point feature histogram (PFH) [44] is a method that finds four features using the normal vector of a plane obtained through principal component analysis (PCA) and the point itself by considering one point and its k-neighbors. Fast point feature histogram (FPFH) [45] is an improved algorithm based on PFH. The original PFH has a complexity of \(O(nk^{2})\) with \(n\) points and \(k\) neighboring points. FPFH addresses the complexity issue by first calculating the relationships between points and neighbors and then assigning weights, reducing the complexity to \(O(nk)\) and enabling faster computations. Radius-based surface descriptor (RSD) [46] focuses on the shape of surfaces among features. It classifies surfaces into types, such as plane, edge, corner, cylinder, and sphere. Signatures of a histogram of orientations (SHOT) [47] collect information based on the direction and curvature of the object's surface in point cloud data, creating a histogram that represents unique features.