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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Bo Li


Wen-jie Zhao


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.5 P.760-776


Novel 3D point set registration method based on regionalized Gaussian process map reconstruction

Author(s):  Bo Li, Yu Zhang, Wen-jie Zhao, Ping Li

Affiliation(s):  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   jameslb20@hotmail.com, zhangyu80@zju.edu.cn, zhaowenjie8@zju.edu.cn, pli@iipc.zju.edu.cn

Key Words:  Point set registration, Gaussian process, Intelligent unmanned system (IUS)

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.

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author="Bo Li, Yu Zhang, Wen-jie Zhao, Ping Li",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%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
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%N 5
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%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900457

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

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.



摘要:点集配准问题是移动智能无人系统领域一项重要研究课题。本文提出一种全新"帧到地图"式三维点集配准方法。首先提出区域化高斯过程(Gaussian process,简称GP)地图重构算法,由此得到一种全新地图表现形式。将由GP得到的预测值和预测位置组合在一起作为预测点,以此自然地建立起预测点对的对应关系,并使用迭代方式计算位姿变换。利用3组标准点集数据集对该方法进行验证和测试。实验结果表明,相较于迭代最近点算法和正态分布变换两种经典点集配准方法,该方法在精度和效率两方面都有更佳表现。此外,所提的地图重构算法能够在降低内存消耗的同时,提供稠密的类点云地图。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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