Quality-controlled registration of urban MLS point clouds reducing drift effects by adaptive fragmentation

2025-10-27

AI Summary

Large-scale mobile laser scanning (MLS) point cloud registration in urban street scenes faces challenges including non-uniform point density, noise contamination, and severe occlusion. To address these, this paper proposes an adaptive segmentation and optimization-based registration framework. First, a novel semi-spherical segmentation (SSC) preprocessing method is introduced, leveraging orthogonal planar features to achieve optimal trajectory segmentation. Second, a voxelized plane selection strategy is incorporated to enhance robustness and avoid local minima. Third, a plane-voxel-guided generalized ICP (PV-GICP) algorithm is developed for efficient and precise registration. Evaluated on real-world MLS data from Munich’s city center, the framework achieves a mean registration accuracy of 0.009 m while reducing computational time by over 50%. This significantly advances the automation level of large-scale urban 3D modeling and dynamic monitoring.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Segmentation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Reducing drift effects in urban MLS point cloud registration
• Addressing varying density and occlusion in city scans
• Improving accuracy and speed of 3D urban modeling

Innovation

• Adaptive fragmentation reduces drift via Semi-sphere Check preprocessing
• Planar Voxel GICP method improves accuracy and halves computation time
• Workflow achieves sub-centimeter urban point cloud registration accuracy