变尺度点云配准算法

doi:10.16182/j.issn1004731x.joss.201807005

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (7): 2465-2474.doi: 10.16182/j.issn1004731x.joss.201807005

变尺度点云配准算法

孙水发^1,2, 李准¹, 夏坤¹, 施云飞¹, 杨继全², 董方敏^1,*

1. 三峡大学水电工程智能视觉监测湖北省重点实验室, 湖北宜昌 443002;
2. 南京师范大学江苏省三维打印装备与制造重点实验室, 南京 210042

收稿日期:2017-05-25 出版日期:2018-07-10 发布日期:2019-01-08
作者简介:孙水发(1977-),男,江西抚州,博士,教授,研究方向为图像处理、计算机视觉。
基金资助:
国家自然科学基金(61273243),湖北省自然科学基金创新群体项目(2015CFA025),湖北省教育厅科学技术研究计划重点项目(D20151204)

Variable Scale Point Cloud Registration Algorithm

Sun Shuifa^1,2, Li Zhun¹, Xia Kun¹, Shi Yunfei¹, Yang Jiquan², Dong Fangmin^1,*

1. China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, Yichang 443002, China;
2. Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing 210042, China

Received:2017-05-25 Online:2018-07-10 Published:2019-01-08

摘要/Abstract

摘要： 针对三维点云配准中点云尺度不一致导致配准精确度不高的问题,提出基于几何重心和质心距离比不变性的多尺度点云配准算法。对点云进行滤波处理;通过点云数据重心与质心建立点云数据之间的尺度比例计算模型;根据配准误差与尺度真值函数关系,对尺度因子进行逐步细化,结合ICP算法进行配准。针对点云数据中不同的情况进行了对比实验,结果表明：在无噪声情况下,实验点云数据配准误差数量级为10^-12~10^-15;在有噪声情况下,实验点云数据配准误差数量级为10^-4。

关键词: 多尺度, 配准, 重心, 质心, 噪声

Abstract: To address the low registration accuracy issue caused by scale mismatch of two point clouds, a multi-scale point cloud registration algorithm is proposed based on the distance ratio invariance of the geometric center of gravity and centroid. The point cloud is firstly filtered. Then, the scale ratio calculation model of the point cloud data is established by computing the point cloud’s gravity center and centroid. Finally, according to the relationship between the registration error and the scale true value, the scale factor is refined step by step with ICP algorithm. For the noise and the inconsistent point in the point cloud, comparative tests are carried out. The experimental results show that, in the absence of noise, the magnitude of registration error order is 10^-12~10^-15; in the case with noise, the magnitude of registration error order is 10^-4.

Key words: multi-scale, point cloud registration, center of gravity, centroid, noise

中图分类号:

TP391.41

孙水发, 李准, 夏坤, 施云飞, 杨继全, 董方敏. 变尺度点云配准算法[J]. 系统仿真学报, 2018, 30(7): 2465-2474.

Sun Shuifa, Li Zhun, Xia Kun, Shi Yunfei, Yang Jiquan, Dong Fangmin. Variable Scale Point Cloud Registration Algorithm[J]. Journal of System Simulation, 2018, 30(7): 2465-2474.

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变尺度点云配准算法

Variable Scale Point Cloud Registration Algorithm

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