The dense high-rise buildings and multipath effects in urban areas significantly reduce the positioning signal accuracy of laser scanning systems, leading to layering and offset issues in the collected point cloud data on the same road. In order to acquire comprehensive and consistent three-dimensional information on the objects, thereby providing field inspection data for large-scale road traffic network scenarios, in this paper, an improved point cloud registration method is proposed to divide the registration process into two stages: elevation registration and plane registration. Elevation registration takes the ground point cloud as the registration primitive, reduces the number of point clouds through curvature down-sampling, and constrains the feature point sequence with a fixed range to provide a good initial pose for fine registration. The plane registration first inherits the elevation registration parameters, combining the dynamic distance parameters of spherical region step based on the median, using robust multi-scale loss functions to address residual points, effective adjacent point pairs are selected to obtain the spatial transformation matrix, and realizes accurate registration. Experimental results with multiple sets of urban point cloud data show that the root mean square error of point cloud registration can be controlled within 0.06 m, achieving a relatively superior registration accuracy, it can provide detailed prior data for measurement information analysis.

To provide a comprehensive representation of 3-Dimensional (3D) information in large-scale outdoor scenes, multi-platform, multi-sensor, and multi-temporal laser point cloud acquisition and registration technologies have experienced rapid development. However, due to the complexity of outdoor environments and the differences in hardware performance across various observation platforms, significant challenges arise in accurately and efficiently registering multi-source heterogeneous point clouds with inconsistent spatial coordinate systems. These challenges include substantial noise interference, occlusions, missing data, and geometric heterogeneity. In this paper, we propose a heterogeneous point cloud fine registration method based on fully connected graph and heat conduction model. Specifically, we first establish initial correspondences for the classified feature primitives using a Gaussian probability distribution framework. Subsequently, low-level semantic association and rigid transformation compatibility check are employed to rapidly eliminate erroneous matching relationships caused by outliers. As a core step, we develop a homonymous point selection algorithm based on heat conduction simulation to accurately estimate robust correspondences for point cloud candidate pairs with limited overlap and discrete noise. The effectiveness of this approach is attributed to the similarity measure of spatial local geometric structures and global topological distributions provided by the nonlinear heat diffusion Laplacian matrix. Finally, a least-squares model weighted by a residual robust loss function is designed, incorporating facade information to solve for the optimal spatial transformation. Extensive experiments on multiple real-world datasets demonstrate that the proposed method inherits the effectiveness and robustness of geometry-based registration strategies, achieving precise fusion of edge positions in multi-source heterogeneous point clouds, with an average RMSE (Root Mean Square Error) below 0.06m. Compared to existing advanced registration methods (e.g., VGICP, Teaser++, et al.), the proposed method demonstrates outstanding registration performance and holds promising application prospects in fields such as fine-grained 3D reconstruction.