Comparing the Performance of Point Cloud Registration Methods for Landslide Monitoring Using Mobile Laser Scanning Data

Abstract

<p><strong>Abstract.</strong> The aim of the research is to evaluate the performance of the point cloud registration methods using mobile laser scanning data. The point cloud registration methods involved in this research are match bounding-box centres and iterative closest point (ICP). The research began with the two epoch’s mobile laser scanning survey using a Phoenix AL-3-32 system. At the same time, the stereo images of the study area were acquired using UAV Photogrammetric method. Both two epoch point cloud datasets were gone through the pre and post-processing stages to produce the cleaned and geo-referenced point clouds data. The data were then gone through the two registration methods and four Cloud-to-Cloud (C2C) distance methods. The 3D surface deviation results obtained from mobile laser scanning data was compared with the 3D surface deviation results from UAV data that undergoes the same registration and C2C distance computation methods. The study area involved in the research is an active landslide area that was located at Kulim Hi-Tech residential area in Kedah state, Malaysia. The study area exposed to the movement of the land which caused cracked to the buildings and drainages. The findings show that the ICP registration becomes the most suitable method to register point clouds dataset that was acquired using mobile laser scanning system. Among the four C2C distance computation methods that was involved in the testing, the least square plane method was the best method to calculate the distance between two sets of point clouds datasets which in turn gave the best results in the process of detecting the movement of the land in the study area.</p>

Digital Fringe Projection-Based Clamping Force Estimation Algorithm for Railway Fasteners

“…Methods that calculate the distance between points include Hausdorff distance estimation, multiscale model-to-model cloud comparison (M3C2) distance estimation, Wasserstein distance estimation, and Chamfer distance estimation [ 30 , 31 ]. There are also methods that estimate distances by fitting surface models such as planes, by Delaunay triangulation, or using quadratic surfaces in local regions [ 32 ]. Jafari et al [ 30 ] used a method combining direct point-by-point distance measurement and statistical sampling to extract structural deformation information and quantified the strain and stress of mechanical deformation by comparing the results.…”

Necropolis of Palazzone in Perugia: Geomatic data integration for 3D modeling and geomorphology of underground sites

“…The GNSS point‐generated DTM described above was subsequently compared with that obtained by UAV photogrammetry through CloudCompare software, which allows us to manage and analyze point clouds, shapefiles, text files, and meshes (Ahmad Fuad, Yusoff, Ismail, & Majid, 2018; Lague, Brodu, & Lerou, 2013).…”

Copyright Protection of 3D Digitized Artistic Sculptures by Adding Unique Local Inconspicuous Errors by Sculptors

“…The comparison in CloudCompare software was done using iterative closest point (ICP) algorithm for aligning. The iterative closest point (ICP) algorithm is a method for aligning point clouds that does not require the corresponding elements and has high accuracy [76,77]. When the point clouds are aligned, the distance of the characteristic point of the model is measured, which is compared in relation to the corresponding point on the reference model.…”