  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, post-processing 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 state-of-the-art 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 tunnel-like
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
Structure-from-Motion, Bundle Adjustment, and available geometry priors to
robustly estimate camera pose and automatically reconstruct a fully-dense 3D
scene using the least possible number of images in various challenging
tunnel-like 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 tunnel-like environments, even those which are
inaccessible for humans.
