Crack assessment using multi-sensor fusion simultaneous localization and mapping (SLAM) and image super-resolution for bridge inspection

Abstract

The inspection of bridges is increasingly dependent on advanced equipment and algorithms like digital cameras and SfM (Structure from Motion). However, many existing SfM-based bridge inspection methods lack efficiency due to lengthy 3D reconstruction computation times, and digital image resolution often falls short in detecting fine cracks and calculating their widths, mainly influenced by the acquisition equipment. This paper describes a fast and accurate crack assessment method that leverages multi-sensor fusion SLAM (Simultaneous Localization and Mapping) and image super-resolution. Through multi-sensor fusion SLAM, textured point clouds of the bridge structure can be obtained directly, significantly improving efficiency. Furthermore, deep learning-based image super-resolution enhances the precision of crack width calculation. Field tests demonstrate the effectiveness of the proposed methods, showcasing a 94% reduction in scene reconstruction time and a 16% improvement in crack width calculation accuracy.