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CLC number: TP242.6
On-line Access: 2020-05-18
Received: 2019-08-31
Revision Accepted: 2020-02-02
Crosschecked: 2020-03-20
Cited: 0
Clicked: 4621
Citations: Bibtex RefMan EndNote GB/T7714
Novel 3D point set registration method based on regionalized Gaussian process map reconstruction
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Bo Li, Yu Zhang, Wen-jie Zhao, Ping Li. Novel 3D point set registration method based on regionalized Gaussian process map reconstruction[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 760-776.
@article{title="Novel 3D point set registration method based on regionalized Gaussian process map reconstruction",
author="Bo Li, Yu Zhang, Wen-jie Zhao, Ping Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="5",
pages="760-776",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900457"
}
%0 Journal Article
%T Novel 3D point set registration method based on regionalized Gaussian process map reconstruction
%A Bo Li
%A Yu Zhang
%A Wen-jie Zhao
%A Ping Li
%J Frontiers of Information Technology & Electronic Engineering
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%N 5
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%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900457
TY - JOUR
T1 - Novel 3D point set registration method based on regionalized Gaussian process map reconstruction
A1 - Bo Li
A1 - Yu Zhang
A1 - Wen-jie Zhao
A1 - Ping Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 5
SP - 760
EP - 776
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900457
Abstract: point set registration has been a topic of significant research interest in the field of mobile intelligent unmanned systems. In this paper, we present a novel approach for a three-dimensional scan-to-map point set registration. Using gaussian process (GP) regression, we propose a new type of map representation, based on a regionalized GP map reconstruction algorithm. We combine the predictions and the test locations derived from the GP as the predictive points. In our approach, the correspondence relationships between predictive point pairs are set up naturally, and a rigid transformation is calculated iteratively. The proposed method is implemented and tested on three standard point set datasets. Experimental results show that our method achieves stable performance with regard to accuracy and efficiency, on a par with two standard methods, the iterative closest point algorithm and the normal distribution transform. Our mapping method also provides a compact point-cloud-like map and exhibits low memory consumption.
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