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改进的RANSAC算法在三维点云配准中的应用

Improved Random Sampling Consistency Algorithm Employed in Three-Dimensional Point Cloud Registration

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摘要

传统随机抽样一致性(RANSAC)算法只能进行粗配准, 且配准效率低。针对该问题提出一种改进的RANSAC快速点云配准算法。该算法将内部形态描述子算法和快速点特征直方图(FPFH)算法相结合, 得到特征描述子, 然后采用预估计和三维栅格分割法改进RANSAC算法, 最后与传统配准算法采样一致性初始配准算法进行比较。实验结果表明, 本文算法能快速精确地剔除误匹配点, 进行仿射变换矩阵求解, 无需二次配准。本文算法相较于传统配准算法有很大优势, 在大规模三维点云配准中具有很好的稳健性, 并且在保证精度的同时可大幅提高配准效率。

Abstract

The traditional random sampling consistency (RANSAC) algorithm can only perform coarse registration at low efficiency. To address this problem, an improved RANSAC fast point cloud registration algorithm is proposed herein. The proposed algorithm first combines the intrinsic shape signatures and fast point feature histogram algorithms to obtain feature descriptors and then employs pre-estimation and three-dimensional (3D) grid segmentation to improve the RANSAC algorithm. Finally, it is compared with the traditional sample consensus initial alignment algorithm. Our experimental results demonstrate that the proposed algorithm can quickly and accurately eliminate false matching points and solve the affine transformation matrix without secondary registration. In comparison with the traditional registration algorithm, the proposed algorithm demonstrates good robustness in large-scale 3D point cloud registration and significantly improves the registration efficiency while ensuring accuracy.

Newport宣传-MKS新实验室计划
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中图分类号:TP391

DOI:10.3788/lop55.101104

所属栏目:成像系统

基金项目:辽宁省科学技术厅项目(201602616)

收稿日期:2018-04-12

修改稿日期:2018-04-26

网络出版日期:2018-05-09

作者单位    点击查看

刘美菊:沈阳建筑大学信息与控制工程学院, 辽宁 沈阳 110168
王旭东:沈阳建筑大学信息与控制工程学院, 辽宁 沈阳 110168
李凌燕:沈阳建筑大学信息与控制工程学院, 辽宁 沈阳 110168
高恩阳:沈阳建筑大学信息与控制工程学院, 辽宁 沈阳 110168

联系人作者:王旭东(1291661630@qq.com)

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引用该论文

Liu Meiju,Wang Xudong,Li Lingyan,Gao Enyang. Improved Random Sampling Consistency Algorithm Employed in Three-Dimensional Point Cloud Registration[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101104

刘美菊,王旭东,李凌燕,高恩阳. 改进的RANSAC算法在三维点云配准中的应用[J]. 激光与光电子学进展, 2018, 55(10): 101104

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