  Recent research has enabled fixed-wing unmanned aerial vehicles (UAVs) to
maneuver in constrained spaces through the use of direct nonlinear model
predictive control (NMPC). However, this approach has been limited to a priori
known maps and ground truth state measurements. In this paper, we present a
direct NMPC approach that leverages NanoMap, a light-weight point-cloud mapping
framework to generate collision-free trajectories using onboard stereo vision.
We first explore our approach in simulation and demonstrate that our algorithm
is sufficient to enable vision-based navigation in urban environments. We then
demonstrate our approach in hardware using a 42-inch fixed-wing UAV and show
that our motion planning algorithm is capable of navigating around a building
using a minimalistic set of goal-points. We also show that storing a
point-cloud history is important for navigating these types of constrained
environments.
