LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with u...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/8/11/2318 |
| _version_ | 1818935492708663296 |
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| author | Qingyuan Zhu Jinjin Wu Huosheng Hu Chunsheng Xiao Wei Chen |
| author_facet | Qingyuan Zhu Jinjin Wu Huosheng Hu Chunsheng Xiao Wei Chen |
| author_sort | Qingyuan Zhu |
| collection | DOAJ |
| description | When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction. |
| first_indexed | 2024-12-20T05:21:02Z |
| format | Article |
| id | doaj.art-dcd67c69b479468d9b4767312f9d06ae |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-20T05:21:02Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dcd67c69b479468d9b4767312f9d06ae2022-12-21T19:52:01ZengMDPI AGApplied Sciences2076-34172018-11-01811231810.3390/app8112318app8112318LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured TerrainQingyuan Zhu0Jinjin Wu1Huosheng Hu2Chunsheng Xiao3Wei Chen4Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Computer Science & Electronic Engineering, University of Essex, Colchester CO4 3SQ, UKDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, ChinaDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, ChinaWhen 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.https://www.mdpi.com/2076-3417/8/11/2318LIDARpoint cloudsunstructured terrainregistrationimproved ICP algorithm |
| spellingShingle | Qingyuan Zhu Jinjin Wu Huosheng Hu Chunsheng Xiao Wei Chen LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain Applied Sciences LIDAR point clouds unstructured terrain registration improved ICP algorithm |
| title | LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain |
| title_full | LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain |
| title_fullStr | LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain |
| title_full_unstemmed | LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain |
| title_short | LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain |
| title_sort | lidar point cloud registration for sensing and reconstruction of unstructured terrain |
| topic | LIDAR point clouds unstructured terrain registration improved ICP algorithm |
| url | https://www.mdpi.com/2076-3417/8/11/2318 |
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