Time budget evaluation for imagebased reconstruction of sewer shafts In this paper we propose a robust realtime image and sensor based approach for automatic 3d model acquisition of sewer shafts from survey videos captured by a downwardlooking fisheyelens camera while lowering it into the shaft. Our approach is based on Structure from Motion adjusted to the constrained motion and scene, and involves shape recognition techniques in order to obtain the geometry of the scene appropriately. We perform a time budget evaluation for the components of an existing offline application based on previous work and design a realtime application which can be applied during onsite inspection. The methods of our approach are modified so that they can be executed on the GPU. Expensive bundle adjustment is avoided by applying a simple and fast geometric correction of the computed reconstruction which is capable of handling inaccuracies of the intrinsic camera calibration parameters.

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.