一种基于关键点选择的快速点云配准算法

张哲; 许宏丽; 尹辉

doi:doi:10.3788/lop54.121002

激光与光电子学进展, 2017, 54 (12): 121002, 网络出版: 2017-12-11

一种基于关键点选择的快速点云配准算法下载： 1004次

A Fast Point Cloud Registration Algorithm Based on Key Point Selection

论文大纲

张哲许宏丽 ^*尹辉

作者单位

北京交通大学计算机与信息技术学院, 北京 100044

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摘要

为了提高三维点云数据配准的效率, 提出一种基于法向量分布特征的关键点初始匹配与迭代最近点(ICP)的精确配准的两步点云配准算法。首先, 定义点云的邻接区域和法向量分布特征计算模型, 提出基于该模型的关键点选择算法; 其次, 为每个关键点建立局部坐标系, 计算关键点的快速点特征直方图, 使用采样一致性配准算法匹配关键点的特征, 去除错误匹配点, 求解出变换矩阵, 完成初始配准; 最后, 使用ICP算法, 对多视点云的初始配准结果进行精确配准。实验结果表明, 在散乱点云数据和自获取的深度点云数据配准中, 该算法能够在确保配准精度的同时有效提升配准效率。

Abstract

In order to improve the registration efficiency of three-dimensional point cloud, a two-step point cloud registration algorithm is proposed based on the key point initial matching using the normal vector distribution feature and the accurate registration using the iterative closest point (ICP). Firstly, the definition of the adjacency region and the normal vector distribution feature model of point cloud are presented, and a key point selection algorithm is proposed based on the model. Secondly, the fast point feature histograms of key points are calculated using the local coordinate system, and the false matches are eliminated by the sampling conformance registration algorithm. According to the corresponding relation, the rotation and translation matrices are calculated and the initial registration is completed. Finally, the final registration result is obtained using ICP algorithm. The experimental results show that the proposed algorithm can effectively improve the registration efficiency while ensuring the accuracy of the registration in the data of unordered point cloud and the self-acquired depth point cloud.

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张哲, 许宏丽, 尹辉. 一种基于关键点选择的快速点云配准算法[J]. 激光与光电子学进展, 2017, 54(12): 121002. Zhang Zhe, Xu Hongli, Yin Hui. A Fast Point Cloud Registration Algorithm Based on Key Point Selection[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121002.

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