# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology
### Data Preprocessing  
In this study, the purpose of data preprocessing is to establish specific rules for generating standardized semantic point clouds that are suitable for our indoor navigation network extraction algorithm, while also ensuring consistency in the file storage structure. In this study, to ensure that the semantic point cloud data input to the algorithm has good quality, we conducted data preprocessing floor-wise and manually segmented the data to obtain the required semantic point clouds. All data segmentation and semantic labeling are conducted in CloudCompare software, version 2.13 [33]. In association with the space classification in Section 2.2, we conducted data preprocessing according to the characteristics of different navigation spaces, with a particular focus on processing hallways and stairwells, as well as the supplementation of virtual doors. Additionally, the preprocessing step effectively removed noise points from the data, ensuring higher accuracy and reliability of the semantic point clouds. Hallways are important passages connecting different rooms and other indoor areas. Thus, the accurate segmentation and labeling of hallways are important for indoor navigation element extraction and network construction. Our preprocessing of hallways mainly focuses on those with turns (i.e., close to 90\({}^{\circ}\)) in the original data. If a hallway has turns along its centerline, it is treated as a composite hallway. In such cases, composite hallways could complicate the indoor navigation element extraction and construction of navigation networks. Therefore, we segmented these composite hallways into multiple simple, straight hallways without turns, as illustrated in Figure 2.  
In general, stairwells consist mainly of the staircase and landing. The staircase is an important component that connects the topological relationship between different floors. In this study, we mainly preprocessed the data for the staircase part and extracted the staircase elements from the stairwell individually. Additionally, we also extracted the elevator element from the elevator lobby. The illustration of the extracted staircase and elevator is shown in Figure 3.  
Figure 3: The illustration of the extracted staircase and elevator. Figure 2: The illustration of a composite hallway converted into multiple simple, straight hallways without turns. Furthermore, we also extracted the doors corresponding to each type of space, such as the doors leading to office rooms or those associated with hallways. It is important to note that in indoor spaces, there are not only physical doors but also walkable shared boundaries. For example, the walkable shared boundary between two adjacent hallways or the walkable shared boundary between a room and a hallway without a physical door. In the research of [PERSON] et al. (2016), they defined these walkable shared boundaries as virtual doors. Although these virtual doors do not look like physical doors, they play an equally important role in connecting adjacent indoor spaces. In our study, we adopted this definition, using virtual doors to connect adjacent indoor spaces that have walkable shared boundaries. Users can also move from one space to another through these virtual doors. These virtual doors, along with physical doors, are part of the connection space. Through data preprocessing, we individually extracted these doors and virtual doors and saved them in their respective spatial directories. As an example, the extracted doors and virtual doors from the hallway are illustrated in Figure 4. It is worth noting that, because our virtual doors are manually segmented, we did not extract the virtual doors using a uniform thickness standard during the segmentation process. As a result, in some cases, the thickness of the virtual doors may vary. However, this difference does not affect the primary function of the virtual doors in the navigation network, which is to connect adjacent spaces that lack physical doors but have walkable shared boundaries.