Robustness of neural network calibration model for accurate spatial positioning

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. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The present study investigates the robustness of a neural network-based camera calibration method for 3D spatial positioning via machine vision. A dimensionless error attenuation coefficient is proposed to measure the robustness of the calibration model, showing that the neural network model outperforms traditional models in terms of robustness. The neural network model significantly improves the robustness and accuracy of 3D spatial positioning in asymmetric camera layouts and multiple camera joint calibration scenarios.