New semi-automatic 3D registration method for terrestrial laser scanning data of bridge structures based on artificial neural networks

Abstract

Modelling of bridge structures is an important topic due to the increasing need to get accurate information in a visual way. Registration between as-built laser data and global design reference one is necessary to guarantee the validity and dependency of the proposed model. This research presents a new registration method based on artificial neural networks (ANN) methodology. A developed software is implemented to apply ANN and to achieve the research objective. A steel structure bridge is chosen for the application of the proposed system. A point cloud laser data (as built) of the bridge is registered to the design model. The results show that the developed registration method is potential and reliable due to the insignificance of the error between both as-built model and the design model. The assumed level of significance is 95% and the error between as-built model and the design model after the registration process is found to be less than 5%. So, one concludes that the developed method can be used as a registration method for the laser scanning bridge structures. Some recommendations are presented to the future study concerned with using deep learning (DL) to apply the registration process to get better results.