Base Study of Bridge Inspection by Modeling Touch Information Using Light Detection and Ranging

Abstract

In Japan, bridges are inspected via close visual examinations every five years. However, these inspections are labor intensive, and a shortage of engineers and budget constraints will restrict such inspections in the future. In recent years, efforts have been made to reduce the labor required for inspections by automating various aspects of the inspection process. In this study, we proposed and evaluated a method of applying super-resolution technology to obtain precise point cloud information to create distance information images to enable the use of tactile information (e.g., human touch) on the surface to be inspected. We measured the distance to the specimen using LiDAR, generated distance information images, performed super-resolution on the pseudo-created low-resolution images, and evaluated them in comparison with the existing magnification method. The evaluation results suggest that the adaptation of the super-resolution technique is effective in increasing the resolution of the boundary of the distance change.