基于平移域估计的点云全局配准算法

doi:10.11772/j.issn.1001-9081.2016.06.1664

计算机应用 ›› 2016, Vol. 36 ›› Issue (6): 1664-1667.DOI: 10.11772/j.issn.1001-9081.2016.06.1664

基于平移域估计的点云全局配准算法

杨滨华, 赵高鹏, 刘鲁江, 薄煜明

南京理工大学自动化学院, 南京 210094

收稿日期:2015-11-27 修回日期:2016-01-14 出版日期:2016-06-10 发布日期:2016-06-08
通讯作者: 赵高鹏
作者简介:杨滨华(1991-),男,江苏溧阳人,硕士研究生,主要研究方向:三维点云处理、视觉导航;赵高鹏(1983-),男,陕西渭南人,讲师,博士,主要研究方向:图像与视频处理、目标识别与跟踪;刘鲁江(1972-),男,江苏南京人,研究员,主要研究方向:三维点云处理、视觉导航;薄煜明(1965-),男,江苏常熟人,研究员,博士,主要研究方向:图像与视频处理、兵器火控。

Global point cloud registration algorithm based on translation domain estimating

YANG Binhua, ZHAO Gaopeng, LIU Lujiang, BO Yuming

School of Automation, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China

Received:2015-11-27 Revised:2016-01-14 Online:2016-06-10 Published:2016-06-08

摘要/Abstract

摘要： 针对迭代最近点(ICP)算法需要两幅点云具有良好的初始位置,否则易陷入局部最优的问题,提出了一种基于平移域估计的点云全局配准算法。首先分别计算数据点云和模型点云的去模糊主方向点云,利用两者平行于坐标轴的包围盒估计平移域范围;其次利用改进的全局ICP算法在估计出的平移域和[-π,π]³的旋转域中进行全局搜索配准。该算法可以根据待配准点云自适应地估计平移域的大小,进行全局自动配准,配准过程中不需要计算点云的特征信息,所需设置的参数少,对点云的初始位置没有要求。实验结果表明,所提算法能够获取全局优化的精确的配准结果,同时提高了全局配准的效率。

关键词: 点云配准, 主方向点云, 平移域估计, 迭代最近点算法, 全局优化

Abstract: The Iterative Closest Point (ICP) algorithm requires two point clouds to have a good initialization to start, otherwise the algorithm may easily get trapped into local optimum. In order to solve the problem, a novel translation domain estimating based global point cloud registration algorithm was proposed. The translation domain was estimated according to axis-aligned bounding box of calculating the defuzzification principal point clouds of data and model point clouds. With the estimated translation domain and [-π, π]³ rotation domain, an improved globally optimal ICP was used to register for global searching. The proposed algorithm could estimate translation domain adaptively and register globally according to the point clouds for registration. The process of registration did not need to calculate the feature information of point clouds and was efficient for any initialization with less setting parameters. The experimental results show that the proposed algorithm can get accurate registration results of global optimization automatically, and also improve the efficiency of global registration.

Key words: point cloud registration, principal point cloud, translation domain estimating, Iterative Closest Point (ICP) algorithm, global optimization

中图分类号:

TP391.4

杨滨华, 赵高鹏, 刘鲁江, 薄煜明. 基于平移域估计的点云全局配准算法[J]. 计算机应用, 2016, 36(6): 1664-1667.

YANG Binhua, ZHAO Gaopeng, LIU Lujiang, BO Yuming. Global point cloud registration algorithm based on translation domain estimating[J]. Journal of Computer Applications, 2016, 36(6): 1664-1667.

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基于平移域估计的点云全局配准算法

Global point cloud registration algorithm based on translation domain estimating

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