双重下采样增强的点云改进配准算法研究

doi:10.19562/j.chinasae.qcgc.2023.04.005

Abstract

Abstract:

In the field of autonomous driving， the real-time requirement of point cloud registration is high. Existing ICP algorithm and its variants have such problems as high requirements for initial pose and slow registration speed. In order to deal with the above-mentioned problems， an improved fast point cloud registration algorithm is proposed in this paper. Firstly， a double down-sampling method is used to preprocess point cloud data. By this way， the amount of point cloud data can be greatly reduced rapidly， while retaining the original features. Then the Intrinsic Shape Signature （ISS） is introduced to optimize the Super 4-Points Congruent Sets （Super4PCS） algorithm to reduce its time complexity. Finally， the linear least squares optimization-based iterative closest point （ICP） algorithm is used for fast and precise registration. The effectiveness of the algorithm is tested and compared by using the Stanford and autonomous driving Kitti point cloud data. The results show that the proposed algorithm has good robustness， and the registration accuracy and speed are significantly increased compared with the existing algorithms.

Key words: point cloud registration, double down-sampling, ISS, Super4PCS, ICP

Zhongsheng Chen,Chaolin Li,Wang Zuo,Xinglin Hou. Study on Improved Point Cloud Registration Algorithm Enhanced by Double Down-sampling[J].Automotive Engineering, 2023, 45(4): 572-578.

Figures/Tables 7

References 19

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类别	点云模型1	点云模型2
兔子
龙
犰狳

点云模型	精配准时间/s	RMSE/m
兔子1	6.353	8.72214×10^-8
兔子2	5.891	5.02143×10^-8
龙1	11.995	3.35157×10^-8
龙2	8.730	5.0376×10^-8
犰狳1	8.353	1.82126×10^-5
犰狳2	1.056	9.4796×10^-5

配准方法	配准时间/s	RMSE/m
ISS+ICP	15.098	1.212×10^-5
Super4PCS+ICP	17.15	1.70865×10^-6
NDT+ICP	21.586	2.5843×10^-6
SAC-IA+ICP	64.527	1.918×10^-7
本文改进算法	7.196	4.724×10^-8

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Study on Improved Point Cloud Registration Algorithm Enhanced by Double Down-sampling

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