Visualization of Quay Wall through Simultaneous Measurement of Multiple Image Sensors Mounted on Unmanned Surface Vehicle

In this study, we developed an Unmanned Surface Vehicle (USV)-based monitoring system for simultaneous data acquisition of both the abovewater and underwater portions of a quay wall structure. To simultaneously acquire point cloud data for both the abovewater and underwater portions, we mounted LiDAR, stereo camera, and sonar on the USV platform. We developed processing and alignment techniques for the measured multi-sensor data. Field experiments were conducted on quay walls to evaluate their applicability for assessing the external surface condition of quay walls. LiDAR point cloud data, measured differently depending on the structure's material, allowed for the identification of the quay wall's surface condition by creating contrast differences in the visualization data. Stereo camera point cloud data provided the most similar representation of shape and color information. While the resolution of the underwater portion was lower than that of the abovewater portion, the sonar data allowed for the identification of underwater conditions, such as the size and shape of riprap. Because the data acquired by LiDAR, stereo camera, and sonar are point cloud data devices with embedded coordinate information, they were able to calculate the size of objects or sections of interest. Further research is needed to determine optimal operating conditions for acquiring high-resolution visualization data.