Semantically-Driven Adaptive Registration for Correcting Non-Constant Drift in Multi-Temporal MLS Data

Abstract. Mobile Laser Scanning (MLS) provides high-accuracy 3D point clouds essential for road infrastructure monitoring. However, multi-temporal MLS analysis is often limited by non-constant, spatially varying trajectory drift caused by GNSS outages and IMU inaccuracies. These misalignments can exceed the magnitude of the changes being monitored, such as pavement deformation, making accurate change detection challenging. This paper presents a fully automatic, semantically driven registration pipeline designed to correct spatially varying drift in directly georeferenced MLS data. The method first applies Principal Component Analysis (PCA) and intensity-based filtering to classify points into stable geometric categories, including flat horizontal surfaces, flat vertical structures, and linear vertical features. A correspondence-based filtering step removes dynamic objects and temporal changes to ensure that registration is driven by stable geometry. The core of the method is an adaptive piecewise registration strategy, where the reference point cloud is divided into sequential 1-meter patches. Each patch is assigned a local rigid transformation estimated using an adaptively expanding registration window guided by the availability of stable vertical features. A final smoothing step ensures spatial continuity between adjacent transformations. The method was evaluated on two MLS datasets collected one year apart along a 3 km road corridor using the FGI Roamer-R4DW system. Validation using 30 independent ground signals showed that the 3D RMSE improved from 3.38 cm to 1.54 cm, with vertical RMSE improving from 2.54 cm to 0.67 cm. The results demonstrate that the proposed approach enables centimeter-level alignment suitable for high-precision multi-temporal road monitoring and change detection applications.