基于空间一致性的同平台点云配准方法

doi:10.11947/j.AGCS.2023.20220262

测绘学报 ›› 2023, Vol. 52 ›› Issue (5): 789-797.doi: 10.11947/j.AGCS.2023.20220262

基于空间一致性的同平台点云配准方法

张广运¹, 韩一¹, 张荣庭¹, 李明峰¹, 吉文来²

1. 南京工业大学测绘科学与技术学院, 江苏南京 211816;
2. 南京工业大学建筑设计研究院, 江苏南京 210009

收稿日期:2022-04-19 修回日期:2023-02-03 发布日期:2023-05-27
通讯作者: 韩一 E-mail:hany_njtech@163.com
作者简介:张广运(1983-),男,博士,教授,硕士生导师,研究方向为成像光谱遥感和LiDAR。E-mail:gyzhang1234@163.com
基金资助:
国家自然科学基金(41974214);江苏省自然资源发展专项资金(海洋科技创新)(JSZRIIYKJ0202101)

A spatial consistency-based point cloud registration method for the same platform

ZHANG Guangyun¹, HAN Yi¹, ZHANG Rongting¹, LI Mingfeng¹, JI Wenlai²

1. School of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China;
2. Architectural Design and Research Institute, Nanjing Tech University, Nanjing 210009, China

Received:2022-04-19 Revised:2023-02-03 Published:2023-05-27
Supported by:
The National Natural Science Foundation of China (No. 41974214);Natural Resources Funds(Ocean Technology Innovation) Project of Jiangsu(No. JSZRIIYKJ202101)

摘要/Abstract

摘要： 基于特征的点云配准技术利用点云局部特征建立对应点,但受噪声和重复结构影响,对应点结果不可避免地包含了大量错误匹配,导致配准精度降低。为充分利用离散点间几何信息优化同平台点云配准精度,本文提出了基于空间一致性的同平台点云配准方法,该方法通过增加候选匹配点构建同平台点云图模型,为获取图模型的最优匹配结果,提出了重加权随机游走匹配(reweight random walks matching,RRWM)的优化方法。基于假设验证方法构建了点云配准模型,并结合试验验证了本文方法的有效性。

关键词: 点云配准, 特征匹配, 离群值滤波, 图匹配

Abstract: The feature-based point cloud registration establishes the correspondence by using the feature descriptor. However, due to the influence of noise and repetitive structure, there will inevitably be a large number of mismatches, resulting in a fall in registration accuracy. In this paper, a spatial consistency-based registration (SCR) method was developed for the point cloud from the same platform. SCR makes full advantage of geometric information between discrete points to improve the point cloud registration accuracy. The graph model of the point cloud that is collected by the same platform is constructed by increasing the number of candidates, and an optimization of reweight random walks matching (RRWM) was proposed to obtain the optimal result. The point cloud registration was built using the hypothesis-and-verify approach. Comprehensive experiments demonstrate that the proposed SCR algorithm is effective in registration methods.

Key words: point cloud registration, feature matching, outlier filtering, graph matching

中图分类号:

P232

张广运, 韩一, 张荣庭, 李明峰, 吉文来. 基于空间一致性的同平台点云配准方法[J]. 测绘学报, 2023, 52(5): 789-797.

ZHANG Guangyun, HAN Yi, ZHANG Rongting, LI Mingfeng, JI Wenlai. A spatial consistency-based point cloud registration method for the same platform[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(5): 789-797.

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基于空间一致性的同平台点云配准方法

A spatial consistency-based point cloud registration method for the same platform

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