Voxel Grid-Based Fast Registration of Terrestrial Point Cloud
Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obta...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/10/1905 |
| _version_ | 1827692627056656384 |
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| author | Biao Xiong Weize Jiang Dengke Li Man Qi |
| author_facet | Biao Xiong Weize Jiang Dengke Li Man Qi |
| author_sort | Biao Xiong |
| collection | DOAJ |
| description | Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS. |
| first_indexed | 2024-03-10T11:27:38Z |
| format | Article |
| id | doaj.art-3fe6ef6c786c4cffb2a58e4343209869 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:27:38Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3fe6ef6c786c4cffb2a58e43432098692023-11-21T19:32:43ZengMDPI AGRemote Sensing2072-42922021-05-011310190510.3390/rs13101905Voxel Grid-Based Fast Registration of Terrestrial Point CloudBiao Xiong0Weize Jiang1Dengke Li2Man Qi3School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, ChinaCollege of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, ChinaTerrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS.https://www.mdpi.com/2072-4292/13/10/1905TLS4PCSvoxel gridkeypoint |
| spellingShingle | Biao Xiong Weize Jiang Dengke Li Man Qi Voxel Grid-Based Fast Registration of Terrestrial Point Cloud Remote Sensing TLS 4PCS voxel grid keypoint |
| title | Voxel Grid-Based Fast Registration of Terrestrial Point Cloud |
| title_full | Voxel Grid-Based Fast Registration of Terrestrial Point Cloud |
| title_fullStr | Voxel Grid-Based Fast Registration of Terrestrial Point Cloud |
| title_full_unstemmed | Voxel Grid-Based Fast Registration of Terrestrial Point Cloud |
| title_short | Voxel Grid-Based Fast Registration of Terrestrial Point Cloud |
| title_sort | voxel grid based fast registration of terrestrial point cloud |
| topic | TLS 4PCS voxel grid keypoint |
| url | https://www.mdpi.com/2072-4292/13/10/1905 |
| work_keys_str_mv | AT biaoxiong voxelgridbasedfastregistrationofterrestrialpointcloud AT weizejiang voxelgridbasedfastregistrationofterrestrialpointcloud AT dengkeli voxelgridbasedfastregistrationofterrestrialpointcloud AT manqi voxelgridbasedfastregistrationofterrestrialpointcloud |