A Robotic Approach for Crack Detection through the Integration of Cameras and LiDARs

Structural health monitoring is crucial for ensuring the safety of civil infrastructure, and crack detection is an essential component of this process. Cameras provide high-resolution images of the structure's surface, which can be analyzed to detect and locate cracks. LiDAR sensors use laser beams to scan the surface of the structure and produce detailed 3D point clouds that can be used to detect cracks and measure their dimensions. The proposed approach aims to improve the accuracy and efficiency of crack detection in SHM by integrating the complementary strengths of cameras and LiDARs in a simulation environment. The approach involves the use of an intelligent algorithm that can automatically fuse the data from the cameras and LiDARs to produce a more comprehensive and accurate representation of surface cracks. The algorithm uses a machine learning-based crack detection technique that can accurately identify and locate cracks in real-time. Furthermore, a depth camera is used to provide a denser point cloud than LiDAR of the crack. The integration of cameras and LiDARs for crack detection in SHM offers several advantages, such as improved accuracy, faster data acquisition, and reduced costs compared to traditional methods. The proposed approach addresses the challenges of data fusion, image processing, and intelligent algorithm development by offering a novel solution that leverages the strengths of both cameras and LiDARs. The findings of this study suggest that the proposed approach can significantly enhance the capabilities of SHM for crack detection. The approach offers a more accurate and efficient way of detecting cracks in real-time, which can help prevent further damage and ensure the safety of civil infrastructure.