Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites
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 thre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/3/819 |
| _version_ | 1811307392325910528 |
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| author | Reza Maalek Derek D Lichti Janaka Y Ruwanpura |
| author_facet | Reza Maalek Derek D Lichti Janaka Y Ruwanpura |
| author_sort | Reza Maalek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:03:31Z |
| format | Article |
| id | doaj.art-bc4b1f37cc534207b0ce2b43d1c02ec4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:03:31Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bc4b1f37cc534207b0ce2b43d1c02ec42022-12-22T02:53:03ZengMDPI AGSensors1424-82202018-03-0118381910.3390/s18030819s18030819Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction SitesReza Maalek0Derek D Lichti1Janaka Y Ruwanpura2Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaAutomated 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.http://www.mdpi.com/1424-8220/18/3/819TLS/LiDAR point cloudsrobust statisticsminimum covariance determinant (MCD)robust principal components analysis (PCA)robust planar and linear segmentation |
| spellingShingle | Reza Maalek Derek D Lichti Janaka Y Ruwanpura Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites Sensors TLS/LiDAR point clouds robust statistics minimum covariance determinant (MCD) robust principal components analysis (PCA) robust planar and linear segmentation |
| title | Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites |
| title_full | Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites |
| title_fullStr | Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites |
| title_full_unstemmed | Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites |
| title_short | Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites |
| title_sort | robust segmentation of planar and linear features of terrestrial laser scanner point clouds acquired from construction sites |
| topic | TLS/LiDAR point clouds robust statistics minimum covariance determinant (MCD) robust principal components analysis (PCA) robust planar and linear segmentation |
| url | http://www.mdpi.com/1424-8220/18/3/819 |
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