Point Cloud Surface Matching Method Based on Precise Matching of Critical Point

doi:10.16182/j.issn1004731x.joss.22-0146

Abstract

Abstract:

To solve the low matching efficiency and insufficient accuracy of feature-based point cloud surface matching method during critical point matching, a point cloud surface matching method based on the pairing exaction of critical points is proposed. An improved 3D scale-invariant feature transform(3D-SIFT) algorithm based on curvature information is presented to extract the critical points. Fast point feature histograms(FPFH) feature, the angle between the vector from the center to critical points and the principal direction of the model are taken as the constraints to obtain the exact critical point matching point pair set. The initial matching of the model surface is implemented by the rigid body transformation parameters, and further the accurate matching of the model surface is achieved by iterative closest point(ICP). Experiments show that the approach can not only improve the critical point matching accuracy, but also enhance the matching efficiency. Compared with other methods, the method is slightly better on the matching speed.

Key words: scale-invariant feature transformation, fast point feature histogram, principal direction, rigid body transformation parameters, iterative closest point

CLC Number:

TP391

Xiaojuan Ning, Chunxu Li, Jiahao Wang, Jing Tang, Yinghui Wang, Haiyan Jin. Point Cloud Surface Matching Method Based on Precise Matching of Critical Point[J]. Journal of System Simulation, 2023, 35(6): 1169-1182.

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模型	算法	牛	龙	茶壶
原始	3D-SIFT	4.350	3.540	3.381
原始	Harris	12.186	11.564	23.836
旋转平移	3D-SIFT	13.419	10.391	13.013
旋转平移	Harris	15.416	15.970	20.287
尺度缩小	3D-SIFT	8.973	9.185	7.122
尺度缩小	Harris	13.111	12.044	48.784

模型	旋转平移	尺寸缩小
模型	改进前/改进后	改进前/改进后
鸟	83.6/88.5	100/100
牛	74.3/84.0	91.9/92.8
龙	29.0/66.4	70.1/89.5

算法	鸟	牛	龙
FPFH	281.028	57.525	406.530
ICP^[15]	19.764	2.024	9.404
改进的ICP算法^[24]	1.608	1.131	1.390
本文	62.451	22.741	116.958

模型	匹配方法	时间/s	旋转误差	平移误差
帽子	ICP^[15]	1.125	19.4	0.012 7
	基于Marvin的点云匹配^[25]	0.761	8.7	0.002 9
	本文算法	11.290	11.9	0.000 7
椅子	ICP^[15]	1.279	16.2	0.023 7
	基于Marvin的点云匹配^[25]	0.828	7.0	0.002 4
	本文算法	13.225	11.8	0.000 3

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