吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (8): 1918-1925.doi: 10.13229/j.cnki.jdxbgxb20220263

• 通信与控制工程 • 上一篇    

基于超体素双向最近邻距离比的点云配准方法

李雪梅1,2(),王春阳1,3(),刘雪莲3,施春浩1,李国瑞1   

  1. 1.长春理工大学 电子信息工程学院，长春 130022
    2.白城师范学院 机械与控制工程学院，吉林 白城 137000
    3.西安工业大学 西安市主动光电成像探测技术重点实验室，西安 710021
  • 收稿日期:2022-03-19 出版日期:2022-08-01 发布日期:2022-08-12
  • 通讯作者: 王春阳 E-mail:lixuemei556677@163.com;wangchunyang19@163.com
  • 作者简介:李雪梅（1973-），女，副教授，博士. 研究方向：光电信息处理. E-mail：lixuemei556677@163.com
  • 基金资助:
    吉林省教育厅科学技术研究重点规划项目(JJKH20220014KJ)

Point cloud registration method based on supervoxel bidirectional nearest neighbor distance ratio

Xue-mei LI1,2(),Chun-yang WANG1,3(),Xue-lian LIU3,Chun-hao SHI1,Guo-rui LI1   

  1. 1.School of Electronic and Information Engineering，Changchun University of Science and Technology，Changchun 130022，China
    2.School of Mechanical and Control Engineering，Baicheng Normal University，Baicheng 137000，China
    3.Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology，Xi'an Technological University，Xi'an 710021，China
  • Received:2022-03-19 Online:2022-08-01 Published:2022-08-12
  • Contact: Chun-yang WANG E-mail:lixuemei556677@163.com;wangchunyang19@163.com

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摘要：

针对点云数据冗余和配准精度低的问题，提出了一种基于超体素的双向最近邻距离比匹配的配准方法。首先，利用超体素提取了具有稳定结构的目标特征点，同时提出了利用点云厚度分层进行非迭代的阈值去噪方法；然后，利用FPFH进行特征描述，提出了双向最近邻距离比方法对点云进行了初始配准；最后，提出了基于双级阈值的点云精确配准方法。采用标准数据库模型进行仿真分析，验证了算法的有效性。结果表明：本方法能有效剔除漂移噪声体素，配准精度高，鲁棒性强。与其他方法对比，在配准时间相近时，本文算法的配准精度提高74.2%；在噪声占比为6%和10%时，配准精度均提高67%以上。

关键词: 信号与信息处理, 点云配准, 超体素, 点云分层去噪, 双向最近邻距离比, 双级阈值配准

Abstract:

Aiming at the problems of redundancy and low registration accuracy of the point cloud data， a bidirectional nearest neighbor distance ratio registration method based on supervoxel is proposed in this paper. Firstly， the target feature points with stable structure are extracted by the supervoxel， and a non-iterative threshold denoising method based on point cloud thickness stratification is proposed； then， using FPFH for feature description， and a bidirectional nearest neighbor distance ratio method is proposed to register the point cloud； finally， an accurate point cloud registration method based on two-level threshold is proposed. The standard database model is used for simulation analysis to verify the effectiveness of the algorithm. The results show that the proposed method in this paper can effectively eliminate the drift noise voxels， and the algorithm has high registration accuracy and strong robustness. Compared with other methods， when the registration time is close， the registration accuracy of this algorithm is improved by 74.2%； When the noise ratio is 6% and 10%， the registration accuracy is improved by more than 67%.

Key words: signal and information processing, point cloud registration, supervoxel, point cloud layered denoising, bidirectional nearest neighbor distance ratio, two-level threshold registration

中图分类号: 

  • G202

图1

超体素生成过程"

图2

体素云分层示意图"

图3

FPFH直方图"

图4

初始配准"

图5

双级阈值配准"

表1

配准参数"

参数名称参数值参数名称参数值
体素分辨率0.001距离比阈值0.985
网格分辨率0.008初配迭代30
法向量阈值0.8初配搜索半径0.05
邻域值10网格化去噪搜索半径0.0015
阈值噪声系数0.9成像横向分辨率0.1
测距精度0.1

表2

配准时间对比"

分组算法总时间/s误差
1FPFH+双向 最近邻距离比 初配+ICP77.69071.4281×10-33
FPFH+双向 最近邻距离比 初配+双级 阈值精配76.89233.6786×10-34
2FPFH+单向 最近邻距离初配19.94594.2109×10-2
FPFH+双向最 近邻距离比初配72.66512.4472×10-4

图6

无噪声下配准结果对比"

图7

噪声下配准结果对比"

表3

噪声下配准精度对比"

算 法噪声占比/%误 差
ICP06.3903×10-4
61.2049×10-4
101.1139×10-4
FPFH+ICP01.7161×10-33
61.5233×10-3
102.3226×10-3
FPFH+双级阈值精配03.7351×10-34
65.2333×10-4
105.6391×10-4
本文算法06.5561×10-5
63.8025×10-5
103.5942×10-5
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