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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have investigated a variety of methods for PCR, the applications have been limited by low accuracy, high memory footprint, and slow speed, especially for dealing with a large number of point cloud data. To solve these problems, a novel local descriptor is proposed for efficient PCR. We formed a comprehensive description of local geometries with their statistical properties on a normal angle, dot product of query point normal and vector from the point to its neighborhood point, the distance between the query point and its neighborhood point, and curvature variation. Sub-features in descriptors were low-dimensional and computationally efficient. Moreover, we applied the optimized sample consensus (OSAC) algorithm to iteratively estimate the optimum transformation from point correspondences. OSAC is robust and practical for matching highly self-similar features. Experiments and comparisons with the commonly used descriptor were conducted on several synthetic datasets and our real scanned bridge data. The result of the simulation experiments showed that the rotation angle error was below 0.025° and the translation error was below 0.0035 m. The real dataset was terrestrial laser scanning (TLS) data of Sujiaba Bridge in Chongqing, China. The results showed the proposed descriptor successfully registered the practical TLS data with the smallest errors. The experiments demonstrate that the proposed method is fast with high alignment accuracy and achieves a better performance than previous commonly used methods.

Details

Title
Establishment and Extension of a Fast Descriptor for Point Cloud Registration
Author
Zhao, Lidu 1 ; Xiang, Zhongfu 1 ; Chen, Maolin 2   VIAFID ORCID Logo  ; Ma, Xiaping 3   VIAFID ORCID Logo  ; Zhou, Yin 1 ; Zhang, Shuangcheng 4   VIAFID ORCID Logo  ; Hu, Chuan 1 ; Hu, Kaixin 5 

 School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China 
 School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China; Key Laboratory of Urban Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518308, China 
 College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China 
 College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China 
 Chongqing Smart City and Sustainble Development Academy, Chongqing 401135, China 
First page
4346
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2711484334
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.