Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The entire registration process is a coarse-to-fine procedure. First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process. To achieve fine matching, 2D registration is performed by extracting 2D boundary points from patches, followed by 3D adjustment. Finally, the results of multiple patch pose estimates are clustered and fused to determine the final pose. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments. In order to compare the robustness of cross-source point cloud registration, the proposed method and generalized iterative closest point method are compared. Furthermore, a metric for describing the degree of point cloud filling is proposed. The experimental results demonstrate that our approach achieves state-of-the-art performance in cross-source point cloud registration.

翻译：近年来，来自不同传感器的跨源点云配准已成为重要的研究热点。然而，由于跨源点云密度与结构存在差异，传统方法面临诸多挑战。为解决这些问题，本文提出了一种名为HybridFusion的跨源点云融合算法。该算法能够对室外大场景中不同视角下的跨源稠密点云进行配准，整个配准过程遵循从粗到细的策略。首先，将点云划分为小块点云，基于全局描述符与空间分布选择匹配块集合，完成粗匹配过程。为实现精细匹配，通过提取小块点云的二维边界点进行二维配准，随后进行三维调整。最后，对多个小块点云位姿估计结果进行聚类融合，确定最终位姿。通过定性与定量实验对所提方法进行了全面评估。为比较跨源点云配准的鲁棒性，将本方法与广义迭代最近点方法进行了对比，并提出了一种描述点云填充程度的度量指标。实验结果表明，本方法在跨源点云配准中达到了最先进的性能。