Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites

Abstract

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

“…To this end, this paper provides a new robust context-based framework for the extraction of primary structural components, namely column, slab, and rebar, in regular rectangular reinforced concrete structures from unorganized point cloud data for automated progress monitoring and dimensional conformity control during construction.Remote Sens. 2019, 11, 1102 3 of 23 actual location of objects comply, which cannot be a presupposition since the objective of automated monitoring and control is to determine the discrepancies between the planned and actual location of each object [22]. Therefore, the studies presented in the following subsections aimed to reduce the dependency of the semantic object extraction on the details of the planned BIM.…”

Concrete Preliminary Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds through Systematic Threshold Definition

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Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data

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