A Multistation 3D Point Cloud Automated Global Registration and Accurate Positioning Method for Railway Tunnels

Terrestrial laser scanning (TLS) technology has the advantages of wide range, high efficiency, and low cost in spatial information collection, so it is widely used in infrastructure monitoring and measurement. During TLS application, the registration and positioning of the point cloud have a direct impact on the quality of the data and the validity of the results. The linear distribution of the tunnel structure and the lack of significant features present challenges in the registration and positioning of 3D point clouds in railway tunnels. The commonly used registration methods are difficult to achieve high registration accuracy and are prone to propagation errors, which reduce the accuracy and effectiveness of results. To achieve accurate registration and positioning of multistation clouds in railway tunnels, we propose a coordinate-based global registration method. To determine the coordinates of scan points in the reference coordinate system and the direction of the reference coordinate system, a few fixed control points are used during the data collection stage. Consequently, each station cloud can be precisely positioned and automatically registered in the reference coordinate system without accumulating or propagating errors. In addition, the coordinate-based registration method eliminates the introduction of errors due to artificial target setting and feature point extraction, as well as the problem of accurately positioning the entire point cloud in the reference coordinate system, thereby enhancing the accuracy, efficiency, and automation levels of cloud registration. The experiment demonstrates that the coordinate-based global registration method is robust and applicable in complex scenes, and it is suitable for the accurate positioning and registration of multistation clouds in linear and curved railway tunnels. The coordinate-based registration method reduces the amount of error in the global registration link by 65% when compared to the point-based registration method, and the point cloud accuracy has reached fine registration, ensuring that fine-grained inverse modeling of the tunnel structure can be performed.