基于ICP算法的非合作目标特征点云配准优化

doi:10.16182/j.issn1004731x.joss.20-FZ0478

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (12): 2383-2387.doi: 10.16182/j.issn1004731x.joss.20-FZ0478

基于ICP算法的非合作目标特征点云配准优化

魏亮¹, 薛牧遥², 霍炬^1*, 张金杰¹

1.哈尔滨工业大学电气工程及自动化学院,黑龙江哈尔滨 150001;
2.上海航天动力技术研究所,上海 201109

收稿日期:2020-05-31 修回日期:2020-07-12 出版日期:2020-12-18 发布日期:2020-12-16
作者简介:魏亮(1993-),男,河北唐山,博士生,研究方向为视觉测量;薛牧遥(1985-),男,上海,硕士,高工,研究方向为固体火箭发动机总体设计。
基金资助:
装备预研与航天科技联合基金(6141B061505)

Non-cooperative Target Feature Point Cloud Registration Optimization Based on ICP Algorithm

Wei Liang¹, Xue Muyao², Huo Ju^1*, Zhang Jinjie¹

1. School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China;
2. Shanghai Space Propulsion Technology Research Institute,Shanghai 201109,China

Received:2020-05-31 Revised:2020-07-12 Online:2020-12-18 Published:2020-12-16

摘要/Abstract

摘要： 针对视觉测量中非合作目标无法提供合作信息从而引起的位姿测量问题,采用ICP(Iterative Closest Point)算法配准不同时刻获取的点云降采样数据来完成目标的相对位姿测量。利用运动恢复结构算法获取了目标当前时刻的点云数据并比较了基于阈值匹配与光流匹配的特征点匹配算法,利用三角测量法对提取到的特征点进行重建,同时将点云数据进行降采样处理,根据降采样后的点云数据计算出不同时刻目标的相对位姿关系。实验表明当物体发生旋转运动时,对降采样数据利用ICP算法计算得到的目标绕坐标轴旋转角度最大误差不超过0.11º。

关键词: 运动恢复结构, 点云匹配, 点云降采样, 迭代最近点

Abstract: Aiming at the pose measurement caused by non-cooperative targets in visual measurement that cannot provide cooperation information,the ICP(Iterative Closest Point) algorithm is used to register the point cloud down-sampling data acquired at different times to complete the relative pose measurement of the target.The point cloud data of the target at the current moment is obtained using the structure from motion algorithm and the feature point matching algorithms are compared based on threshold matching and optical flow matching method.The extracted feature points are reconstructed by triangulation.The relative pose changes of the object at different times are calculated by using the downsampling point cloud data.Experiments show that when the object rotates,the maximum error of the rotation angle using the ICP algorithm does not exceed 0.11º.

Key words: structure from motion, point cloud matching, point cloud downsampling, iterative closest point

中图分类号:

TP391.9

魏亮, 薛牧遥, 霍炬, 张金杰. 基于ICP算法的非合作目标特征点云配准优化[J]. 系统仿真学报, 2020, 32(12): 2383-2387.

Wei Liang, Xue Muyao, Huo Ju, Zhang Jinjie. Non-cooperative Target Feature Point Cloud Registration Optimization Based on ICP Algorithm[J]. Journal of System Simulation, 2020, 32(12): 2383-2387.

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基于ICP算法的非合作目标特征点云配准优化

Non-cooperative Target Feature Point Cloud Registration Optimization Based on ICP Algorithm

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