Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1918-1925.doi: 10.13229/j.cnki.jdxbgxb20220263

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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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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

CLC Number: 

  • G202

Fig.1

Supervoxel generation process"

Fig.2

Schematic chart of voxel cloud stratification"

Fig.3

FPFH histogram"

Fig.4

Initial registration"

Fig.5

Two-level threshold registration"

Table 1

Registration parameters"

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

Table 2

Comparison of registration time"

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

Fig.6

Comparison of registration results without noise"

Fig.7

Comparison of registration results with noise"

Table 3

Comparison of registration accuracy with noise"

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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