# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
###### Abstract
Therefore, the manual modeling result may differ from the real scene, failing to meet the requirements for high-precision tunnel perception. Currently, research on point cloud-based tunnel modeling focuses on the modeling of underground pipelines, road and railway tunnels, etc. [24; 25]. These scenarios have more regular tunnel shapes, less noise, and fewer occlusions compared to coal mine tunnel scenes, as shown in Figure 1b. Therefore, it is necessary to study point cloud-based modeling methods for coal mine tunnels. Figure 1: The difficulties of coal mine tunnel modeling. (**a**) Tunneling errors. (**b**) Equipment occlusion. To address the above issues, this paper proposes a method for modeling coal mine tunnels based on point clouds, as shown in Figure 2, which mainly includes the following steps:  
(1) Point Cloud Preprocessing: Given the complexity of tunnels, which often contain various devices that contribute to data redundancy and occlusion, preprocessing is essential to ensure the quality of the model. The raw point cloud data is collected, and algorithms are applied for point cloud coordinate transformation, tunnel point cloud extraction, and tunnel point cloud completion, as shown in Figure 2a-c. This step ensures that the data accurately represents the tunnel structure, free from irrelevant details. (2) Tunnel Centerline Extraction: The tunnel centerline is an important reference for the movement and precise positioning of devices in the tunnel, and it is also a reference for optimizing the normal vectors of point clouds in subsequent steps. A novel approach for extracting the tunnel centerline is developed, using a convex hull method to achieve high precision, as shown in Figure 2d. (3) Optimization of Point Cloud Normals: Accurate point cloud normals are vital for Poisson surface reconstruction. This paper introduces an algorithm that optimizes these normals by ensuring they consistently point outward from the tunnel surface, as shown in Figure 2e. This optimization, guided by the previously extracted centerline, enhances the modeling quality. (4) Point Cloud Triangulation Modeling: The final step involves creating a triangulated mesh model of the tunnel using the Poisson reconstruction method [15], as shown in Figure 2f. This triangulated surface represents a detailed and continuous model of the tunnel, which accurately reflects its geometry while maintaining computational efficiency. The primary contributions of this paper include:  
(1) We propose a novel point cloud data extraction and completion method specifically designed for coal mine tunnels, which enhances model accuracy and completeness by addressing data redundancy and occlusion caused by various industrial equipment. This method employs advanced preprocessing techniques to ensure that the point cloud accurately reflects the tunnel structure, free from irrelevant details. (2) To facilitate precise operations within the tunnels, we introduce a new centerline extraction algorithm that utilizes a convex hull method for high-precision extraction. This algorithm also serves as a critical reference for optimizing the normal vectors of point clouds, thereby improving the subsequent modeling process. Figure 2: The framework of tunnel modeling. (3) By integrating the point cloud extraction and centerline extraction methods, we present a tunnel modeling framework for coal mine tunnels. This framework encompasses steps for point cloud preprocessing, normal optimization, and triangulated mesh modeling, significantly enhancing both modeling accuracy and computational efficiency. The subsequent structure of this paper is outlined as follows. In Section 2, an overview of the existing research on point cloud modeling is presented. Section 3 specifically introduces the details of each step illustrated in Figure 2, including coordinate transformation, point cloud data extraction, completion, centerline extraction, normal optimization, and triangulated mesh modeling. Section 4 presents the experimental data and results.