Towards improved targetless registration and deformation analysis of TLS point clouds using patch-based segmentation

Abstract

The geometric changes in the real world can be captured by measuring and comparing the 3D coordinates of object surfaces. Traditional point-wise measurements with low spatial resolution may fail to detect inhomogeneous, anisotropic and unexpected deformations, and thus cannot reveal complex deformation processes. 3D point clouds generated from laser scanning or photogrammetric techniques have opened up opportunities for an area-wise acquisition of spatial information. In particular, terrestrial laser scanning (TLS) exhibits rapid development and wide application in areal geodetic monitoring owing to the high resolution and high quality of acquired point cloud data. However, several issues in the process chain of TLS-based deformation monitoring are still not solved satisfactorily. This thesis mainly focuses on the targetless registration and deformation analysis of TLS point clouds, aiming to develop novel data-driven methods to tackle the current challenges. For most deformation processes of natural scenes, in some local areas no shape deformations occur (i.e., these areas are rigid), and even the deformation directions show a certain level of consistency when these areas are small enough. Further point cloud processing, like stability and deformation analyses, could benefit from the assumptions of local rigidity and consistency of deformed point clouds. In this thesis, thereby, three typical types of locally rigid patches - small planar patches, geometric primitives, and quasi-rigid areas - can be generated from 3D point clouds by specific segmentation techniques. These patches, on the one hand, can preserve the boundaries between rigid and non-rigid areas and thus enable spatial separation with respect to surface stability. On the other hand, local geometric information and empirical stochastic models could be readily determined by the points in each patch. Based on these segmented rigid patches, targetless registration and deformation analysis of deformed TLS point clouds can be improved regarding accuracy and spatial resolution. Specifically, small planar patches like supervoxels are utilized to distinguish the stable and unstable areas in an iterative registration process, thus ensuring only relatively stable points are involved in estimating transformation parameters. The experimental results show that the proposed targetless registration method has significantly improved the registration accuracy. These small planar patches are also exploited to develop a novel variant of the multiscale model-to-model cloud comparison (M3C2) algorithm, which constructs prisms extending from planar patches instead of the cylinders in standard M3C2. This new method separates actual surface variations and measurement uncertainties, thus yielding lower-uncertainty and higher-resolution deformations. A coarse-to-fine segmentation framework is used to extract multiple geometric primitives from point clouds, and rigorous parameter estimations are performed individually to derive high-precision parametric deformations. Besides, a generalized local registration-based pipeline is proposed to derive dense displacement vectors based on segmented quasi-rigid areas that are corresponded by areal geometric feature descriptors. All proposed methods are successfully verified and evaluated by simulated and/or real point cloud data. The choice of proposed deformation analysis methods for specific scenarios or applications is also provided in this thesis.