Three-Dimensional Measurement Method of Four-View Stereo Vision Based on Gaussian Process Regression

Sensors

Zhijiang Zhang¹  Miao Gong¹  Tao Peng¹  Dan Zeng¹ 

[1] Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai 200444, China;

关键词: multisensor system;    gaussian process regression;    bayesian reasoning method;   

DOI  :  10.3390/s19204486

来源: DOAJ

【 摘 要 】

Multisensor systems can overcome the limitation of measurement range of single-sensor systems, but often require complex calibration and data fusion. In this study, a three-dimensional (3D) measurement method of four-view stereo vision based on Gaussian process (GP) regression is proposed. Two sets of point cloud data of the measured object are obtained by gray-code phase-shifting technique. On the basis of the characteristics of the measured object, specific composite kernel functions are designed to obtain the initial GP model. In view of the difference of noise in each group of point cloud data, the weight idea is introduced to optimize the GP model, which is the data fusion based on Bayesian inference method for point cloud data. The proposed method does not require strict hardware constraints. Simulations for the curve and the high-order surface and experiments of complex 3D objects have been designed to compare the reconstructing accuracy of the proposed method and the traditional methods. The results show that the proposed method is superior to the traditional methods in measurement accuracy and reconstruction effect.

【 授权许可】

Unknown