Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction
For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-01-01
|
| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940231193022 |
| _version_ | 1797385585357225984 |
|---|---|
| author | Weidong Zhao Dandan Zhang Dan Li Yao Zhang Qiang Ling |
| author_facet | Weidong Zhao Dandan Zhang Dan Li Yao Zhang Qiang Ling |
| author_sort | Weidong Zhao |
| collection | DOAJ |
| description | For iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed. |
| first_indexed | 2024-03-08T21:56:17Z |
| format | Article |
| id | doaj.art-b66e7c3a8d924d5d9d573f790844c122 |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-03-08T21:56:17Z |
| publishDate | 2024-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-b66e7c3a8d924d5d9d573f790844c1222023-12-19T20:03:48ZengSAGE PublishingMeasurement + Control0020-29402024-01-015710.1177/00202940231193022Optimized GICP registration algorithm based on principal component analysis for point cloud edge extractionWeidong ZhaoDandan ZhangDan LiYao ZhangQiang LingFor iterative closest point (ICP) algorithm, the initial position and the number of iterations are needed in registration. At the same time, the ICP algorithm is easy to fall into local convergence and convergence speed is slow. By constructing K-D tree to search neighborhood points and artificially set threshold, plane fitting is carried out, the on-time point cloud to be deployed is separated from the complex background, and statistical analysis is used to calculate the distance between the point cloud and the neighborhood point to quickly remove the invalid point cloud. The surface equation is set to calculate the tangent plane of point cloud normal vector and each normal vector, and the local coordinate system is constructed. The angle between adjacent vectors and the local coordinate system is calculated to determine the feature point set of edge contour. According to the covariance matrix of the feature points set, the principal feature component is obtained, the principal axis direction of the two sets of point clouds is found, and the rotation matrix and the displacement vector are obtained. Finally, GICP precise registration of point cloud is carried out according to initial pose parameters and rigid body transformation matrix obtained by maximum likelihood estimation method. The results show that the optimized algorithm can effectively avoid local convergence. Compared with the traditional ICP algorithm, when the algorithm achieves the same registration accuracy in the public dataset experiment, the registration speed is on average 44.82% faster and the overlap rate is on average 15.26% higher. In the real dataset experiment, the registration speed is on average 59.04% faster, the registration accuracy is on average 30.24% higher and the overlap rate is on average 10.61% higher. This shows that the optimization algorithm is superior to the traditional ICP algorithm in registration accuracy and convergence speed.https://doi.org/10.1177/00202940231193022 |
| spellingShingle | Weidong Zhao Dandan Zhang Dan Li Yao Zhang Qiang Ling Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction Measurement + Control |
| title | Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction |
| title_full | Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction |
| title_fullStr | Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction |
| title_full_unstemmed | Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction |
| title_short | Optimized GICP registration algorithm based on principal component analysis for point cloud edge extraction |
| title_sort | optimized gicp registration algorithm based on principal component analysis for point cloud edge extraction |
| url | https://doi.org/10.1177/00202940231193022 |
| work_keys_str_mv | AT weidongzhao optimizedgicpregistrationalgorithmbasedonprincipalcomponentanalysisforpointcloudedgeextraction AT dandanzhang optimizedgicpregistrationalgorithmbasedonprincipalcomponentanalysisforpointcloudedgeextraction AT danli optimizedgicpregistrationalgorithmbasedonprincipalcomponentanalysisforpointcloudedgeextraction AT yaozhang optimizedgicpregistrationalgorithmbasedonprincipalcomponentanalysisforpointcloudedgeextraction AT qiangling optimizedgicpregistrationalgorithmbasedonprincipalcomponentanalysisforpointcloudedgeextraction |