Robust Registration of Multi-Source Terrain Point Clouds via Region-Aware Adaptive Weighting and Cauchy Residual Control

Multi-source topographic point clouds are of great value in applications such as mine monitoring, geological hazard assessment, and high-precision terrain modeling. However, challenges such as heterogeneous data sources, drastic terrain variations, and significant differences in point density severely hinder accurate registration. To address these issues, this study proposes a robust point cloud registration method named Cauchy-AdaV2, which integrates region-adaptive weighting with Cauchy-based residual suppression. The method jointly leverages slope and roughness to partition terrain into regions and constructs a spatially heterogeneous weighting function. Meanwhile, the Cauchy M-estimator is employed to mitigate the impact of outlier correspondences, enhancing registration accuracy while maintaining adequate correspondence coverage. The results indicate that the proposed method significantly outperforms traditional ICP, GICP, and NDT methods in terms of overall error metrics (MAE, RMSE), error control in complex terrain regions, and cross-sectional structural alignment. Specifically, it achieves a mean absolute error (MAE) of 0.0646 m and a root mean square error (RMSE) of 0.0688 m, which are 70.5% and 72.4% lower than those of ICP, respectively. These outcomes demonstrate that the proposed method possesses stronger spatial consistency and terrain adaptability. Ablation studies confirm the complementary benefits of regional and residual weighting, while efficiency analysis shows the method to be practically applicable in large-scale point cloud scenarios. This work provides an effective solution for high-precision registration of heterogeneous point clouds, especially in challenging environments characterized by complex terrain and strong disturbances.