# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 1 Introduction  
With the rise in advanced technologies like Internet of Things (IoT) and augmented reality (AR), there is a growing demand for efficient and accurate indoor navigation systems that can support a wide range of applications [1, 2, 3], from guiding users through complex buildings to ensuring safe, wheelchair-accessible pathways. At the core of these systems is the need for precise indoor navigation models, which represent the spatial and semantic structure of indoor environments. In recent years, various indoor navigation models have been developed, mainly including three types [4, 5]: the network model [6, 7, 8, 9], the grid/voxel model [10, 11], and the space entity model [12]. These models, along with their development methods, each have unique advantages and limitations in representing complex indoor environments, making it essential to choose both the appropriate model and construction method based on the specific requirements of different navigation applications, such as low cost, highefficiency in development, and robust performance in real-world applications. For the network model, its simplicity, flexibility, and efficiency have made it widely favored for constructing indoor navigation systems. Moreover, recent advancements in 3D mapping technologies, along with the development of deep learning, have opened new avenues for the automatic extraction of indoor navigation network models [2, 13, 14]. In the field of 3D mapping, the availability of low-cost sensor devices such as the Apple smart devices [15, 16], Microsoft HoloLens [17, 18, 19], and depth cameras [20, 21, 22] allows for an increasing number of users, even those without professional knowledge, to perform 3D mapping and obtain indoor spatial information [15]. This not only reduces the cost of indoor mapping, but also allows more non-experts to participate. Furthermore, with the rapid development of deep learning technologies [23, 24, 25], it has become possible to train 3D point cloud semantic segmentation models that can automatically recognize and classify various elements as semantic point clouds within indoor spaces with a reasonable degree of accuracy. Semantic point clouds, which contain detailed geometric and semantic information about indoor environments, offer significant potential for extracting indoor navigation elements. However, their quality can vary substantially due to differences in data acquisition methods, annotation techniques, and segmentation models, often resulting in inconsistencies, noise, and incomplete semantic labels. Consequently, extracting accurate navigation networks from such point clouds often requires manual intervention during the preprocessing stage. However, to the best of our knowledge, no standardized preprocessing framework currently exists to guide this process. While research on extracting indoor navigation networks from semantic point clouds remains limited, some progress has been made. For example, [PERSON] et al. [26] proposed a semantics-guided method for reconstructing indoor navigation networks directly from RGB-D sensor data. However, their research does not fully address the challenges posed by multi-floor environments, particularly the accurate representation of transitional spaces such as staircases and elevators. The transitional spaces are critical for seamless vertical connectivity and play a crucial role in various real-world applications. For example, in robotics, an accurate navigation network of such spaces facilitates autonomous navigation across multiple floors. Additionally, in emergency response systems, precise information about transitional spaces enables responders to quickly navigate complex indoor environments, thereby improving the success rate of rescue operations. Specifically, staircases present a notable challenge in accurately extracting the direction and layout of staircases in an automated manner due to their complex geometries, variable step structures, and possible direction changes, especially for unstructured point cloud data.