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Sensors 2018, 18(3), 819; https://doi.org/10.3390/s18030819

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

1
Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Received: 1 December 2017 / Revised: 10 February 2018 / Accepted: 5 March 2018 / Published: 8 March 2018
(This article belongs to the Special Issue Terrestrial Laser Scanning)
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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. View Full-Text
Keywords: TLS/LiDAR point clouds; robust statistics; minimum covariance determinant (MCD); robust principal components analysis (PCA); robust planar and linear segmentation TLS/LiDAR point clouds; robust statistics; minimum covariance determinant (MCD); robust principal components analysis (PCA); robust planar and linear segmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Maalek, R.; Lichti, D.D.; Ruwanpura, J.Y. Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites. Sensors 2018, 18, 819.

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