Fast Registration of Point Cloud Based on Custom Semantic Extraction
With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answer...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7479 |
| _version_ | 1797476895900565504 |
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| author | Jianing Wu Zhang Xiao Fan Chen Tianlin Peng Zhi Xiong Fengwei Yuan |
| author_facet | Jianing Wu Zhang Xiao Fan Chen Tianlin Peng Zhi Xiong Fengwei Yuan |
| author_sort | Jianing Wu |
| collection | DOAJ |
| description | With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude. |
| first_indexed | 2024-03-09T21:10:14Z |
| format | Article |
| id | doaj.art-8d7168f2469444e3b64a4d1223e049ec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:14Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8d7168f2469444e3b64a4d1223e049ec2023-11-23T21:49:49ZengMDPI AGSensors1424-82202022-10-012219747910.3390/s22197479Fast Registration of Point Cloud Based on Custom Semantic ExtractionJianing Wu0Zhang Xiao1Fan Chen2Tianlin Peng3Zhi Xiong4Fengwei Yuan5School of Mechanical Engineering, University of South China, Hengyang 421001, ChinaSchool of Mechanical Engineering, University of South China, Hengyang 421001, ChinaSchool of Wealth Management, Ningbo University of Finance & Economics, Ningbo 315000, ChinaSchool of Mechanical Engineering, University of South China, Hengyang 421001, ChinaSchool of Computer Science, University of Glasgow, Glasgow G12 8RZ, UKSchool of Mechanical Engineering, University of South China, Hengyang 421001, ChinaWith the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.https://www.mdpi.com/1424-8220/22/19/7479point cloud segmentation3D feature extractionlocal featureslocal domain selectionregional semantic scoring |
| spellingShingle | Jianing Wu Zhang Xiao Fan Chen Tianlin Peng Zhi Xiong Fengwei Yuan Fast Registration of Point Cloud Based on Custom Semantic Extraction Sensors point cloud segmentation 3D feature extraction local features local domain selection regional semantic scoring |
| title | Fast Registration of Point Cloud Based on Custom Semantic Extraction |
| title_full | Fast Registration of Point Cloud Based on Custom Semantic Extraction |
| title_fullStr | Fast Registration of Point Cloud Based on Custom Semantic Extraction |
| title_full_unstemmed | Fast Registration of Point Cloud Based on Custom Semantic Extraction |
| title_short | Fast Registration of Point Cloud Based on Custom Semantic Extraction |
| title_sort | fast registration of point cloud based on custom semantic extraction |
| topic | point cloud segmentation 3D feature extraction local features local domain selection regional semantic scoring |
| url | https://www.mdpi.com/1424-8220/22/19/7479 |
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