Open AccessRobustness of neural network calibration model for accurate spatial positioning
Author(s) -
Jianyu Dou,
Chong Pan,
Jianhua Liu
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.438539
Subject(s) - robustness (evolution) , computer science , artificial neural network , artificial intelligence , calibration , pinhole camera model , computer vision , polynomial and rational function modeling , camera resectioning , algorithm , camera auto calibration , mathematics , polynomial , mathematical analysis , biochemistry , chemistry , statistics , gene
The present study devotes to a systematical exploration for the robustness of neural network-based camera calibration method in the circumstance of three-dimensional (3D) spatial positioning via machine vision technique. By analyzing the error propagation route in the calibration-reconstruction process, a dimensionless error attenuation coefficient is proposed to measure the robustness of a calibration model with respect to input calibration error. Using this metric, the robustness of the neural network (NN) model under different optical configurations, i.e., input noise level, optical distortion and camera viewing angle, are analyzed in detail via synthetic simulation. Due to its generalized fitting capacity, the NN model is found to be superior to conventional pinhole model and polynomial model in terms of model robustness. To take full advantage of this feature, the NN model is further deployed to the scenarios of asymmetric camera layout and multiple camera joint calibration. Both synthetic simulation and experiment test demonstrate that the NN model can significantly improve the robustness and the accuracy of 3D spatial positioning in these non-normal scenarios.