A High Resolution Image Mosaic Method Based on Camera Calibration for Tunnel Patrol Robot System Aiming at the problem of complicated process and limited scope in visual inspection of tunnel defects, a high resolution image mosaic method based on camera calibration for tunnel patrol robot system is proposed. Patroling on the specific track with a high resolution image mosaic module in the tunnel, the designed mobile robot greatly simplifies the defect detection process, and saves a lot of manpower and material resources by using the cylindrical image mosaic method based on fast camera calibration. Through the fast and accurate calibration process, the high resolution panoramic image of the tunnel is obtained without scene constraints, which is beneficial for subsequent comprehensive defect detection. The experimental results show that the camera calibration for high resolution image mosaic system could be done accurately and quickly with tunnel patrol robot run steadily in door and tunnel scenes, and the process of image mosaics is short in time, with high mosaics accuracy and good imaging effect, and strong robustness.

Dense 3D Reconstruction for Visual Tunnel Inspection using Unmanned Aerial Vehicle Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, postprocessing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, our solution can fit the stringent requirement for tunnel inspection, in terms of battery life, size and weight. While the current stateoftheart can estimate camera pose and 3D geometry from a sequence of images, they assume large overlap, small rotational motion, and many distinct matching points between images. These assumptions severely limit their effectiveness in tunnellike scenarios where the camera has erratic or large rotational motion, such as the one mounted on the UAV. This paper presents a novel solution which exploits StructurefromMotion, Bundle Adjustment, and available geometry priors to robustly estimate camera pose and automatically reconstruct a fullydense 3D scene using the least possible number of images in various challenging tunnellike environments. We validate our system with both Virtual Reality application and experimentation with a real dataset. The results demonstrate that the proposed reconstruction along with texture mapping allows for remote navigation and inspection of tunnellike environments, even those which are inaccessible for humans.