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

arXiv: https://arxiv.org/abs/2510.23416 · HTML: https://mlsanity.com/paper/2510.23416/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://mlsanity.com/paper/1605.03344/) (2016)
- [Adaptive Robust Kernels for Non-Linear Least Squares Problems](https://mlsanity.com/paper/2004.14938/) (2020)
- [A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration](https://mlsanity.com/paper/2205.07404/) (2022)
- [Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes](https://mlsanity.com/paper/1905.09634/) (2019)
- [SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds](https://mlsanity.com/paper/2206.13117/) (2022)
- [An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds](https://mlsanity.com/paper/1703.02150/) (2017)

## Abstract (arXiv)

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.