Rust detection from 3D point clouds using sensor fusion

Abstract

In this study, we developed a method leveraging sensor fusion technology that combines camera images and LiDAR point clouds obtained using Matterport for efficient inspection of rust in conduit tunnels. Conventional methods relying solely on point cloud data showed difficulty in detecting rust due to its minimal surface irregularities and shape changes. In contrast, high-precision rust detection was achieved by utilizing differences in color and texture from camera images, enabling the estimation of rust locations within tunnels. By integrating information from images and LiDAR, it became possible to calculate values such as rust location and area, which are difficult to estimate from images alone. The sensor fusion approach accurately estimated the distance per image pixel, achieving a mean absolute error of 1.3×10⁻³ m and a mean absolute percentage error (MAPE) of 6.7%.