This study presents a novel workflow designed to efficiently and accurately register large-scale mobile laser scanning (MLS) point clouds to a target model point cloud in urban street scenarios. This workflow specifically targets the complexities inherent in urban environments and adeptly addresses the challenges of integrating point clouds that vary in density, noise characteristics, and occlusion scenarios, which are common in bustling city centers. Two methodological advancements are introduced. First, the proposed Semi-sphere Check (SSC) preprocessing technique optimally fragments MLS trajectory data by identifying mutually orthogonal planar surfaces. This step reduces the impact of MLS drift on the accuracy of the entire point cloud registration, while ensuring sufficient geometric features within each fragment to avoid local minima. Second, we propose Planar Voxel-based Generalized Iterative Closest Point (PV-GICP), a fine registration method that selectively utilizes planar surfaces within voxel partitions. This pre-process strategy not only improves registration accuracy but also reduces computation time by more than 50% compared to conventional point-to-plane ICP methods. Experiments on real-world datasets from Munich's inner city demonstrate that our workflow achieves sub-0.01 m average registration accuracy while significantly shortening processing times. The results underscore the potential of the proposed methods to advance automated 3D urban modeling and updating, with direct applications in urban planning, infrastructure management, and dynamic city monitoring.

翻译：本研究提出了一种新颖的工作流程，旨在高效、准确地将大规模移动激光扫描点云配准到目标模型点云，适用于城市街道场景。该工作流程专门针对城市环境固有的复杂性，并巧妙地解决了点云密度、噪声特征和遮挡场景变化带来的挑战，这些挑战在繁华的城市中心区域尤为常见。研究引入了两项方法学改进。首先，提出的半球面检查预处理技术通过识别相互正交的平面表面，对MLS轨迹数据进行优化分段。这一步骤减少了MLS漂移对整个点云配准精度的影响，同时确保每个分段内具有足够的几何特征以避免局部最优。其次，我们提出了基于平面体素的广义迭代最近点法，这是一种精细配准方法，选择性地利用体素分区内的平面表面。与传统的点对面ICP方法相比，这种预处理策略不仅提高了配准精度，还将计算时间减少了50%以上。在慕尼黑内城区的真实数据集上进行的实验表明，我们的工作流程实现了低于0.01米的平均配准精度，同时显著缩短了处理时间。结果强调了所提方法在推动自动化三维城市建模与更新方面的潜力，可直接应用于城市规划、基础设施管理和动态城市监测。