Hierarchical Optimization of 3D Point Cloud Registration
Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/23/6999 |
| _version_ | 1797545460778401792 |
|---|---|
| author | Huikai Liu Yue Zhang Linjian Lei Hui Xie Yan Li Shengli Sun |
| author_facet | Huikai Liu Yue Zhang Linjian Lei Hui Xie Yan Li Shengli Sun |
| author_sort | Huikai Liu |
| collection | DOAJ |
| description | Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance. |
| first_indexed | 2024-03-10T14:15:44Z |
| format | Article |
| id | doaj.art-91592b4846d145d7a9c15a30fc04b66f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:15:44Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-91592b4846d145d7a9c15a30fc04b66f2023-11-20T23:47:18ZengMDPI AGSensors1424-82202020-12-012023699910.3390/s20236999Hierarchical Optimization of 3D Point Cloud RegistrationHuikai Liu0Yue Zhang1Linjian Lei2Hui Xie3Yan Li4Shengli Sun5Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaShanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaShanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaShanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaShanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaShanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaRigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.https://www.mdpi.com/1424-8220/20/23/69993D point cloud registrationimproved voxel filtermulti-scale voxelized GICP |
| spellingShingle | Huikai Liu Yue Zhang Linjian Lei Hui Xie Yan Li Shengli Sun Hierarchical Optimization of 3D Point Cloud Registration Sensors 3D point cloud registration improved voxel filter multi-scale voxelized GICP |
| title | Hierarchical Optimization of 3D Point Cloud Registration |
| title_full | Hierarchical Optimization of 3D Point Cloud Registration |
| title_fullStr | Hierarchical Optimization of 3D Point Cloud Registration |
| title_full_unstemmed | Hierarchical Optimization of 3D Point Cloud Registration |
| title_short | Hierarchical Optimization of 3D Point Cloud Registration |
| title_sort | hierarchical optimization of 3d point cloud registration |
| topic | 3D point cloud registration improved voxel filter multi-scale voxelized GICP |
| url | https://www.mdpi.com/1424-8220/20/23/6999 |
| work_keys_str_mv | AT huikailiu hierarchicaloptimizationof3dpointcloudregistration AT yuezhang hierarchicaloptimizationof3dpointcloudregistration AT linjianlei hierarchicaloptimizationof3dpointcloudregistration AT huixie hierarchicaloptimizationof3dpointcloudregistration AT yanli hierarchicaloptimizationof3dpointcloudregistration AT shenglisun hierarchicaloptimizationof3dpointcloudregistration |