面向盾牌形貌重建的双层采样点云粗配准方法

doi:10.16180/j.cnki.issn1007-7820.2024.07.008

Abstract

Abstract:

In view of the problems of low precision and time-consuming point cloud coarse registration in the process of 3D reconstruction of shield surface topography, this study proposes a double-layer sampling point cloud coarse registration algorithm based on the traditional RANSAC(Random Sample Consensus) point cloud coarse registration algorithm framework. In each iteration of the first layer of the proposed algorithm, the single-point sampling is used for rigid constraints to reduce the size of the corresponding set, which is regarded as the "interior point candidate set" of the second layer. The second layer of the algorithm performs continuous random sampling of two points and computes their respective minimum models. The optimal rigid body transformation matrix is obtained by iteratively maximizing the consistent set and using the least square method. In the experimental stage, 6 groups of shield point clouds with different degrees of down sampling are used, and the RANSAC algorithm and the proposed algorithm are used to conduct a point cloud coarse registration comparison experiment. Experimental results show that the proposed algorithm is superior to the RANSAC algorithm in both rough registration speed and rough registration accuracy. The registration speed is about twice that of the RANSAC algorithm, and the root-mean-square error of the proposed algorithm is 10^-3 mm, which is nearly 400 times higher than the RANSAC algorithm.

Key words: topography reconstruction, rough registration, RANSAC, double-layer sampling, single point sampling, rigid constraint, consensus set, shield

CLC Number:

TP391

WAN Shengrui, ZHOU Zhifeng, WU Minghui, WANG Liduan, ZHOU Wei. Coarse Registration Method of Double-Layer Sampling Point Cloud for Shield Topography Reconstruction[J].Electronic Science and Technology, 2024, 37(7): 60-65.

Figures/Tables 11

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References 18

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配准算法	点云数	时间/s	均方根误差/mm
RANSAC算法	99 675	4.475	2.917
本文算法	99 675	2.175	0.328×10^-3
RANSAC算法	88 608	4.960	0.708
本文算法	88 608	2.170	2.339×10^-3
RANSAC算法	73 746	5.450	0.607
本文算法	73 746	2.091	2.331×10^-3
RANSAC算法	63 999	4.221	1.590
本文算法	63 999	1.623	2.798×10^-3
RANSAC算法	54 968	3.808	1.226
本文算法	54 968	1.345	1.724×10^-3
RANSAC算法	45 899	4.173	3.111
本文算法	45 899	1.204	0.898×10^-3

Coarse Registration Method of Double-Layer Sampling Point Cloud for Shield Topography Reconstruction

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