删除或更新信息，请邮件至freekaoyan#163.com(#换成@)

基于关键点特征匹配的点云配准方法

本站小编 Free考研考试/2021-12-21
本文二维码信息
二维码（扫一下试试看！）
基于关键点特征匹配的点云配准方法
Point Cloud Registration Method Based on Key Point Extraction with Small Overlap
投稿时间：2018-11-18
DOI：10.15918/j.tbit1001-0645.2018.476
中文关键词:点云配准关键点提取SHOT低重叠率
English Keywords:point cloud registrationkey point extractionSHOTlow overlapping rate
基金项目:黑龙江省自然科学基金资助项目（F201123）
作者单位E-mail
陆军哈尔滨工程大学 自动化学院, 黑龙江, 哈尔滨 150001
邵红旭哈尔滨工程大学 自动化学院, 黑龙江, 哈尔滨 150001
王伟哈尔滨工程大学 自动化学院, 黑龙江, 哈尔滨 150001348359423@qq.com
范哲君哈尔滨工程大学 自动化学院, 黑龙江, 哈尔滨 150001
夏桂华哈尔滨工程大学 自动化学院, 黑龙江, 哈尔滨 150001
摘要点击次数:885
全文下载次数:315
中文摘要:
针对ICP配准算法对点云的初始位置要求高、处理低重叠率的点云配准能力低的问题，提出了一种基于关键点特征匹配的点云配准方法. 设计一种多尺度加权法向投影均值差的关键点提取算法，结合SHOT描述子对关键点进行特征描述，融合几何一致性以及RANSAC算法去除匹配过程中的误匹配点对，优化关键点之间的对应关系，通过奇异值分解计算刚体变换矩阵，完成点云粗配准，使用ICP进行精确配准. 实验表明，本文提出的关键点提取算法能有效提取点云表面特征变化明显的点，使用SHOT特征对关键点进行描述，能够快速、精确地完成点云数据配准，并且对于较低重叠率的点云，也具有较好的配准效果.
English Summary:
The ICP registration algorithm has high requirements for the initial position of point clouds and low registration ability for point clouds with low overlapping rate.To solve these problems, a point cloud registration method based on feature matching of key points was proposed.A key point extraction algorithm based on the differences of mean values of multiscale weighted normal projection was designed, and then key points were characterized by SHOT descriptors.Fusing of geometric consistency and RANSAC algorithm, the mismatched point pairs were removed in the matching process, and the correspondences between key points were optimized.The transformation matrix was obtained by using singular value decomposition, cloud rough registration was completed, and fine registration was performed by using ICP.The experiment results show that the key point extraction algorithm proposed in this paper can effectively extract points with obvious changes in the surface of the point cloud.And using SHOT descriptor to characterize the key points can complete registration of point cloud data quickly and accurately.At the same time, for the point cloud with low overlapping rate, the proposed method also has better registration effect.
查看全文查看/发表评论下载PDF阅读器
相关话题/哈尔滨工程大学 自动化 黑龙江 中文 数据

Free考研考试FreeKaoYan.Com
欢迎来到Free考研考试，"为实现人生的Free而奋斗"

© 2020 FreeKaoYan! . All rights reserved.