## 1 Introduction
To achieve a comprehensive representation of geographic entities and obtain complete spatial information, the integration of multi-platform sensors is required to compensate for the data loss caused by single-perspective sensors [26; 27]. [PERSON] et al. captured the point cloud of a mine roadway using an RGB-D camera and a TLS system, which solved the problem of point cloud loss caused by the restricted field of view when the underground space is too narrow and compensated for the lack of scene representation of a single laser scanning system [28]. [PERSON] et al. analyzed the point cloud acquisition principles of unmanned aircraft systems (UASs), HLS systems, and TLS systems. The feature differences of multi-platform point clouds of the same target were compared and the feature points of the HLS and UAS point clouds extracted to achieve the restoration of the TLS point cloud, which improved the quality of the TLS point cloud [29]. However, in an underground mine, a laser SLAM point cloud captured by an HLS system is located in the free coordinate system due to the absence of GNSS signals and the control points are not added in the acquisition process. The TLS system adopts a self-defined coordinate inside the instrument, and the coordinate origin is located in the center of the laser scanner. As a result, both the TLS point cloud and HLS point cloud adopt an independent coordinate system, and the spatial datum unification of multi-platform point clouds is an unavoidable problem. Considering the characteristics of high accuracy and high resolution of point clouds, scholars have proposed achieving coregistration by extracting the overlapping area directly from the multi-platform point cloud. According to the kind of features used for registration, coregistration methods can be classified into three categories: feature point-based methods [30; 31], feature line-based methods [32; 33], and feature plane-based methods [34]. These strategies are effective in describing man-made scenes, but in natural environments, they are often limited to capturing specific features, such as the edges, corners, and faces of blocks or joints [35]. Especially for an underground mine, an unstructured, narrow, and enclosed environment, it is difficult to obtain the geodetic coordinates of the fixed monitoring points. Correct convergence cannot be achieved if the feature conditionsare not sufficient or the distribution is not uniform. Featureless point cloud methods are matched directly through the correspondence relationship between point clouds, which mainly include iterative closest point (ICP), 3D-normal distribution transform (3D-NDT), and its improved algorithms. In the work of [PERSON] et al., the ICP and genetic algorithms are combined to register a TLS and vehicle-mounted laser point cloud, which makes up for the shortcomings of scene expression using a single laser scanning system [36]. However, the ICP algorithm directly uses the nearest points of the multi-platform point clouds to establish the correspondence relation, which requires a high degree of overlap and the initial position of the two groups of point clouds, and the global search efficiency is low. The 3D-NDT algorithm divides the reference point cloud into a fixed-size three-dimensional unit cell, discards the ICP algorithm's process of calculating and matching by using the correspondence feature points, which considers the sampling point neighborhood, and calculates a probabilistic statistical model to determine optimal matching through the distance optimization approach [37; 38]. In the work of [PERSON] et al., a multiscale iterative normal distribution transform was applied for the registration of large-scale outdoor scans [39].