Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration
With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/16/3210 |
| _version_ | 1797522150745178112 |
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| author | Shikun Li Ruodan Lu Jianya Liu Liang Guo |
| author_facet | Shikun Li Ruodan Lu Jianya Liu Liang Guo |
| author_sort | Shikun Li |
| collection | DOAJ |
| description | With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality. |
| first_indexed | 2024-03-10T08:25:24Z |
| format | Article |
| id | doaj.art-69635d4dfe7c47fdaa7b2a031c31764d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:25:24Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-69635d4dfe7c47fdaa7b2a031c31764d2023-11-22T09:33:54ZengMDPI AGRemote Sensing2072-42922021-08-011316321010.3390/rs13163210Super Edge 4-Points Congruent Sets-Based Point Cloud Global RegistrationShikun Li0Ruodan Lu1Jianya Liu2Liang Guo3School of Mathematics & Statistics, Shandong University, Weihai 264209, ChinaDarwin College, University of Cambridge, Cambridge CB3 9EU, UKData Science Institute, Shandong University, Jinan 250100, ChinaSchool of Mathematics & Statistics, Shandong University, Weihai 264209, ChinaWith the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.https://www.mdpi.com/2072-4292/13/16/3210global registrationboundary segmentationoverlapping regionsvolumetric information |
| spellingShingle | Shikun Li Ruodan Lu Jianya Liu Liang Guo Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration Remote Sensing global registration boundary segmentation overlapping regions volumetric information |
| title | Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration |
| title_full | Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration |
| title_fullStr | Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration |
| title_full_unstemmed | Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration |
| title_short | Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration |
| title_sort | super edge 4 points congruent sets based point cloud global registration |
| topic | global registration boundary segmentation overlapping regions volumetric information |
| url | https://www.mdpi.com/2072-4292/13/16/3210 |
| work_keys_str_mv | AT shikunli superedge4pointscongruentsetsbasedpointcloudglobalregistration AT ruodanlu superedge4pointscongruentsetsbasedpointcloudglobalregistration AT jianyaliu superedge4pointscongruentsetsbasedpointcloudglobalregistration AT liangguo superedge4pointscongruentsetsbasedpointcloudglobalregistration |