  Navigation of UAVs in challenging environments like tunnels or mines, where
it is not possible to use GNSS methods to self-localize, illumination may be
uneven or nonexistent, and wall features are likely to be scarce, is a complex
task, especially if the navigation has to be done at high speed. In this paper
we propose a novel proof-of-concept navigation technique for UAVs based on the
use of LiDAR information through the joint use of geometric and
machine-learning algorithms. The perceived information is processed by a deep
neural network to establish the yaw of the UAV with respect to the tunnel's
longitudinal axis, in order to adjust the direction of navigation.
Additionally, a geometric method is used to compute the safest location inside
the tunnel (i.e. the one that maximizes the distance to the closest obstacle).
This information proves to be sufficient for simple yet effective navigation in
straight and curved tunnels.
