Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing
With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/2/161 |
| _version_ | 1797542178309799936 |
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| author | Sangmin Oh Dongmin Lee Minju Kim Taehoon Kim Hunhee Cho |
| author_facet | Sangmin Oh Dongmin Lee Minju Kim Taehoon Kim Hunhee Cho |
| author_sort | Sangmin Oh |
| collection | DOAJ |
| description | With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS. |
| first_indexed | 2024-03-10T13:26:58Z |
| format | Article |
| id | doaj.art-4fd2efe80b8e43ccae2610b35da4d45a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:26:58Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4fd2efe80b8e43ccae2610b35da4d45a2023-11-21T08:47:25ZengMDPI AGRemote Sensing2072-42922021-01-0113216110.3390/rs13020161Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region GrowingSangmin Oh0Dongmin Lee1Minju Kim2Taehoon Kim3Hunhee Cho4School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaDepartment of Civil and Environmental Engineering, University of Michigan, 2350 Hayward St., G.G Brown Bldg., Ann Arbor, MI 48109, USASchool of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaWith the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.https://www.mdpi.com/2072-4292/13/2/161building component detectionindoor point cloudmobile laser scannerrandom sample consensusregion growing |
| spellingShingle | Sangmin Oh Dongmin Lee Minju Kim Taehoon Kim Hunhee Cho Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing Remote Sensing building component detection indoor point cloud mobile laser scanner random sample consensus region growing |
| title | Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing |
| title_full | Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing |
| title_fullStr | Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing |
| title_full_unstemmed | Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing |
| title_short | Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing |
| title_sort | building component detection on unstructured 3d indoor point clouds using ransac based region growing |
| topic | building component detection indoor point cloud mobile laser scanner random sample consensus region growing |
| url | https://www.mdpi.com/2072-4292/13/2/161 |
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