Block-to-Point Fine Registration in Terrestrial Laser Scanning

Abstract

Fine registration of point clouds plays an important role in data analysis in Terrestrial Laser Scanning (TLS). This work proposes a block-to-point fine registration approach to correct the errors of point clouds from TLS and of geodetic networks observed using total stations. Based on a reference coordinate system, the block-to-point estimation is performed to obtain representative points. Then, fine registration with a six-parameter transformation is performed with the help of an Iterative Closest Point (ICP) method. For comparisons, fine registration with a seven-parameter transformation is introduced by applying a Singular Value Decomposition (SVD) algorithm. The proposed method not only corrects the registration errors between a geodetic network and the scans, but also considers the errors among the scans. The proposed method was tested on real TLS data of a dam surface, and the results showed that distance discrepancies of estimated representative points between scans were reduced by approximately 60%.

Towards Describing Full-Section Deformations Using Terrestrial Laser Scanning in the Badaling Tunnel (China)

“…) T denotes the coordinates of the identical representative points from the epoch that are moved, and =( , , ) T denotes the coordinates of the identical representative points from the reference epoch. The SVD algorithm is used to compute the transformation parameters within an errors-in-variables (EIV) model (Felus and Burtch, 2008) where coordinates observed at both epochs are treated as stochastic variables (see more details on the adopted algorithm in Wang, 2013). All corresponding representative points from corresponding blocks are processed as identical points.…”

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

“…The method is promising to work well in the proposed three-stage model as the focus is deformation monitoring of structures with surfaces consisting of 3D geometry which is ideal in ensuring convergence to a reliable solution in multi epoch comparisons since the Piecewise Alignment Method is based on the ICP algorithm. Wang (2013) described the Block to Point Method for fine registration in TLS which aimed at improving the quality of registration and reducing the effect of random and systematic errors between the scans in TLS applications. The method worked successfully and it was proposed to be used for deformation monitoring applications as a subject of future research and not only for fine registration.…”

“…The point cloud can also be fitted using functions or surfaces such as planes, spheres, cylinders and the Non-Uniform B-Splines (NURBS) (e.g. Vezocnik et al, 2009;Wang, 2013;Gonzalez Aguilera et al, 2008).…”

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

“…Liang et al [18] also utilized artificial masks for point cloud registration. Wang [19] proposed a block-to-point fine registration approach, where the point clouds are divided into small blocks, and each block is estimated as a representative point. Besides points, lines are also used for point cloud registration.…”