期刊论文

Fengwei Yuan

英文

Sensors

1424-3210

2022

22

19

7479-

10.3390/s22197479

Conceptualization, J.W.; methodology, J.W.; software, J.W.; validation, J.W., F.Y. and Z.X. (Zhang Xiao); formal analysis, F.Y.; investigation, F.C. and T.P.; resources, F.C., T.P. and Z.X. (Zhi Xiong); data curation, F.C. and Z.X. (Zhi Xiong); writing—original draft preparation, J.W.; writing—review and editing, J.W., Z.X. (Zhang Xiao) and F.Y.; visualization, J.W.; supervision, F.Y. and Z.X. (Zhang Xiao); project administration, F.Y.; funding acquisition, F.Y. and Z.X. (Zhang Xiao). All authors have read and agreed to the published version of the manuscript. Supported by the Opening Project of Cooperative Innovation Center for Nuclear Fuel Cycle Technology and Equipment, University of South China (2019KFZ04) and Program of Science and Technology Commissioners of Hunan Province (2021GK5049).

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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 o...