Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System
The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/1/138 |
| _version_ | 1797358231175036928 |
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| author | Sangyoon Park Sungha Ju Minh Hieu Nguyen Sanghyun Yoon Joon Heo |
| author_facet | Sangyoon Park Sungha Ju Minh Hieu Nguyen Sanghyun Yoon Joon Heo |
| author_sort | Sangyoon Park |
| collection | DOAJ |
| description | The latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate local coordinate systems into a unified coordinate system, still relies on manual intervention. To address this practical issue, this study presents an automated point cloud registration approach optimized for a stop-and-go scanning system based on a quadruped walking robot. The proposed approach comprises three main phases: perpendicular constrained wall-plane extraction; coarse registration with plane matching using point-to-point displacement calculation; and fine registration with horizontality constrained iterative closest point (ICP). Experimental results indicate that the proposed method successfully achieved automated registration with an accuracy of 0.044 m and a successful scan rate (SSR) of 100% within a time frame of 424.2 s with 18 sets of scan data acquired from the stop-and-go scanning system in a real-world indoor environment. Furthermore, it surpasses conventional approaches, ensuring reliable registration for point cloud pairs with low overlap in specific indoor environmental conditions. |
| first_indexed | 2024-03-08T14:57:52Z |
| format | Article |
| id | doaj.art-f42be1863c42495bacb17217768e64aa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:52Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f42be1863c42495bacb17217768e64aa2024-01-10T15:08:43ZengMDPI AGSensors1424-82202023-12-0124113810.3390/s24010138Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning SystemSangyoon Park0Sungha Ju1Minh Hieu Nguyen2Sanghyun Yoon3Joon Heo4Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of KoreaDepartment of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of KoreaThe latest advances in mobile platforms, such as robots, have enabled the automatic acquisition of full coverage point cloud data from large areas with terrestrial laser scanning. Despite this progress, the crucial post-processing step of registration, which aligns raw point cloud data from separate local coordinate systems into a unified coordinate system, still relies on manual intervention. To address this practical issue, this study presents an automated point cloud registration approach optimized for a stop-and-go scanning system based on a quadruped walking robot. The proposed approach comprises three main phases: perpendicular constrained wall-plane extraction; coarse registration with plane matching using point-to-point displacement calculation; and fine registration with horizontality constrained iterative closest point (ICP). Experimental results indicate that the proposed method successfully achieved automated registration with an accuracy of 0.044 m and a successful scan rate (SSR) of 100% within a time frame of 424.2 s with 18 sets of scan data acquired from the stop-and-go scanning system in a real-world indoor environment. Furthermore, it surpasses conventional approaches, ensuring reliable registration for point cloud pairs with low overlap in specific indoor environmental conditions.https://www.mdpi.com/1424-8220/24/1/138point cloud registrationstop-and-go scanning systemsterrestrial laser scanning |
| spellingShingle | Sangyoon Park Sungha Ju Minh Hieu Nguyen Sanghyun Yoon Joon Heo Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System Sensors point cloud registration stop-and-go scanning systems terrestrial laser scanning |
| title | Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System |
| title_full | Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System |
| title_fullStr | Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System |
| title_full_unstemmed | Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System |
| title_short | Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System |
| title_sort | automated point cloud registration approach optimized for a stop and go scanning system |
| topic | point cloud registration stop-and-go scanning systems terrestrial laser scanning |
| url | https://www.mdpi.com/1424-8220/24/1/138 |
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