基于区域化高斯过程地图重构的新型三维点集配准方法

李博; 张宇; 赵文杰; 李平

doi:10.1631/FITEE.1900457

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基于区域化高斯过程地图重构的新型三维点集配准方法

智能无人系统专辑 | Updated：2022-05-19

- 基于区域化高斯过程地图重构的新型三维点集配准方法
- Novel 3D point set registration method based on regionalized Gaussian process map reconstruction
- 信息与电子工程前沿（英文） 2020年21卷第5期页码：760-776
- Affiliations：
  
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
  State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Bo LI, E-mail: jameslb20@hotmail.com" ]
  [ "Yu ZHANG, E-mail: zhangyu80@zju.edu.cn" ]
  Wen-jie ZHAO, zhaowenjie8@zju.edu.cn
  [ "Ping LI, E-mail: pli@iipc.zju.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61673341, 61703366, and 11705026)
- DOI：10.1631/FITEE.1900457
  中图分类号： TP242.6
- 收稿：2019-08-31，
  
  修回：2020-;3-20，
  
  纸质出版：2020-05
- Accepted：
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李博, 张宇, 赵文杰, 等. 基于区域化高斯过程地图重构的新型三维点集配准方法[J]. 信息与电子工程前沿（英文）, 2020,21(5):760-776.

Bo LI, Yu ZHANG, Wen-jie ZHAO, et al. 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.
李博, 张宇, 赵文杰, 等. 基于区域化高斯过程地图重构的新型三维点集配准方法[J]. 信息与电子工程前沿（英文）, 2020,21(5):760-776. DOI： 10.1631/FITEE.1900457.

Bo LI, Yu ZHANG, Wen-jie ZHAO, et al. 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. DOI： 10.1631/FITEE.1900457.

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。

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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