Accuracy improvement of multi-view 3D laser scanning measurements based on point cloud error correction and global calibration optimization.

Abstract

Multi-camera laser scanning measurement is emerging as a pivotal element in three-dimensional (3D) optical measurements. It reduces occlusion and enables the gathering of more 3D data. However, it also increases the difficulty of system algorithms in obtaining high measurement accuracy. To improve the measurement accuracy, there is an urgent need to address global calibration and error correction issues caused by the employment of multi-view systems. An accuracy improvement method for multi-view 3D laser scanning measurements based on point cloud error correction and global calibration optimization is then proposed. First, a planar asymmetric circular grid target is designed to calibrate the cameras, laser planes, and initial global transformation matrices of the multi-view 3D laser scanning probe simultaneously. The influence of the position of the laser plane on the measurement error is analyzed and what we believe to be novel mathematical error influencing factors are then modelled for point accuracy. Furthermore, a believed to be novel error model based on the backpropagation (BP) neural network is established for the regression analysis of the mathematical error influencing factors and measurement deviations for each point based on the standard sphere plate measurement. The final measurement is optimized by the correction of point cloud for each camera of the multi-view system and the global calibration optimization based on the error model. The proposed method is reliable and easy to implement, since it only requires a standard sphere plate and a planar target. Several experiments show that the method can effectively improve the measurement accuracy of multi-view 3D laser scanning probe through point cloud error correction and calibration optimization.