Long-term monitoring method for tunnel structure transformation using a 3D light detection and ranging equipped in a mobile robot

This study proposes a long-term health monitoring method for tunnel structure transformation using a 3D light detection and ranging (LiDAR) equipped in a mobile robot. The proposed method comprised two phases: 3D point cloud maps (PCMs) generation and health indicators (HIs) extraction at the same location for the long-term structure transformation inspection. First, the 3D point cloud data (PCD) and six-degree-of-freedom acceleration and gyration of a mobile robot were measured during periodic inspection of tunnel structures using the 3D LiDAR and an inertial measurement unit. This information was combined to generate a 3D PCM by utilizing tightly coupled LiDAR inertial odometry. This procedure could be repeated to inspect tunnel structure transformation at patrol inspection in a long term. 3D PCMs measured during period inspection were then registered to extract a 3D PCD at the location of the first measurement. Hence, an iterative closest point method with edge features was executed to register periodic PCD measurements. Second, 3D PCD in predefined locations were extracted at every measurement. These PCD were used to extract features that are highly correlated to the inner wall and ground of the tunnel structure to obtain five HIs representing their health conditions. These HIs were then employed to evaluate the structural health of the tunnel. Field experiments were conducted to demonstrate the effectiveness of the proposed method in buildings and tunnels. The proposed method outperformed other 3D PCM methods, especially in structures with few features such as tunnels and corridors. A systematic analysis of the experimental results also revealed that the proposed method ensures the accuracy and robustness of HIs extraction at the same location, confirming the feasibility of long-term inspection. The proposed inspection method for tunnel structures and embedded mobile robot system provides a cost-effective and autonomous solution for infrastructure health monitoring.