一种闭合条件约束的全局最优多视点云配准方法

闫利; 谭骏祥; 杨容浩; 李少达

doi:10.11947/j.AGCS.2016.20150018

测绘学报 >

2016 , Vol. 45 >Issue 4: 418 - 424

DOI: https://doi.org/10.11947/j.AGCS.2016.20150018

摄影测量学与遥感

一种闭合条件约束的全局最优多视点云配准方法

闫利 ,
谭骏祥 ,
杨容浩 ,
李少达

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1. 武汉大学测绘学院, 湖北武汉 430079;
2. 成都理工大学地球科学学院, 四川成都 610059

闫利(1966-),男,教授,博士生导师,主要从事摄影测量、遥感和三维激光扫描技术的教学与科研工作。

收稿日期: 2015-01-08

修回日期: 2015-08-10

网络出版日期: 2016-04-28

基金资助

测绘地理信息公益性行业科研专项资助(201512008)

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A Method of Globally Optimal Registration for Multi-view Point Clouds Constrained by Closed-loop Conditions

YAN Li ,
TAN Junxiang ,
YANG Yonghao ,
LI Shaoda

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1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
2. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, ChinaAbstract

Received date: 2015-01-08

Revised date: 2015-08-10

Online published: 2016-04-28

Supported by

The Scientific Research Foundation for Public Walfare Industry of Surveying and Mapping and Geographic Information & Disaster Reduction(No.201512008)

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摘要

针对已有多视点云配准存在的问题,提出了一种严密的闭合条件约束配准方法。该方法首先采用"点-切平面"迭代最近点算法分别求解各对应点云间的坐标转换参数;再以单站点云为配准单元,并将转换参数视为随机观测值构建条件方程,采用条件平差方法对转换参数作改正以达到全局最优。通过地面三维激光扫描仪实测两组点云数据进行试验,验证了该方法有效且可行。试验结果表明,对采样间隔为毫米级和厘米级的点云,增加扫描重叠度能够提高配准精度和可靠性。

关键词： 多视配准; ICP算法; 七参数; 误差累积; 条件平差

本文引用格式

闫利 , 谭骏祥 , 杨容浩 , 李少达 . 一种闭合条件约束的全局最优多视点云配准方法[J]. 测绘学报, 2016 , 45(4) : 418 -424 . DOI: 10.11947/j.AGCS.2016.20150018

Abstract

This paper proposes a rigorous registration method of multi-view point clouds constrained by closed-loop conditions for the problems of existing algorithms. In our approach, the point-to-tangent-plane iterative closest point algorithm is used firstly to calculate coordinate transformation parameters of all adjacent point clouds respectively. Then the single-site point cloud is regarded as registration unit and the transformation parameters are considered as random observations to construct conditional equations, and then the transformation parameters can be corrected by conditional adjustments to achieve global optimum. Two practical experiments of point clouds acquired by a terrestrial laser scanner are shown for demonstrating the feasibility and validity of our methods. Experimental results show that the registration accuracy and reliability of the point clouds with sampling interval of millimeter or centimeter level can be improved by increasing the scanning overlap.

Key words： multi-view registration; iterative closest point algorithm; seven parameters; error accumulation; conditional adjustment

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