Computer Science ›› 2020, Vol. 47 ›› Issue (3): 137-142.doi: 10.11896/jsjkx.190200261

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Adaptive Levenberg-Marquardt Cloud Registration Method for 3D Reconstruction

ZENG Jun-fei,YANG Hai-qing,WU Hao   

  1. (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310000, China)
  • Received:2019-02-02 Online:2020-03-15 Published:2020-03-30
  • About author:ZENG Jun-fei,born in 1993,postgra-duate.His main research interests include visual SLAM and three-dimensional reconstruction. YANG Hai-qing,born in 1971,asso-ciate professor,postgraduate supervisor.His main research interests include computer vision and so on.
  • Supported by:
    This work was supported by the Natural Science Foundation of Zhejiang pvovince, China (LY13F010008) and Science and Technology Plan Project of Zhejiang province, China (2015F50009).

Abstract: To address the problems that point cloud registration process in three-dimensional (3D) reconstruction is susceptible to environmental noise,point cloud exposure,illumination,object occlusion and other factors,as well as the traditional ICP registration algorithm with low accuracy and long time-consuming,this paper proposed a point cloud registration algorithm based on adaptive Levenberg-Marquart.Firstly,the initial point cloud data is pretreated by way of statistical filtering and voxel raster filtering,and then the filtered point cloud is stratified to eliminate the outlier data,so as to improve the accuracy of subsequent point cloud registration.Furthermore,aiming at the problem that traditional point cloud feature description method is computation-intensive,smoothness parameter is adopted to conduct extracting point cloud features and improve the efficiency of point cloud re-gistration.Finally,the point-to-line and point-to-surface constraints between frames are established on the basis of the point cloud features,and the modified Levenberg-Marquardt method is utilized to realize point cloud registration,so as to construct a satis-fying 3D reconstruction model.The experimental results show that the proposed point cloud registration method is suitable for 3D reconstruction of indoor and outdoor scenes,with outstanding environmental adaptability.Meanwhile,the accuracy and efficiency of point cloud registration are greatly improved compared with the traditional methods.

Key words: 3D reconstruction, Point cloud registration, Point cloud feature, Smoothness, Levenberg-Marquardt

CLC Number: 

  • TP391
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