  Drones have been getting more and more popular in many economy sectors. Both
scientific and industrial communities aim at making the impact of drones even
more disruptive by empowering collaborative autonomous behaviors -- also known
as swarming behaviors -- within fleets of multiple drones. In swarming-powered
3D mapping missions, unmanned aerial vehicles typically collect the aerial
pictures of the target area whereas the 3D reconstruction process is performed
in a centralized manner. However, such approaches do not leverage computational
and storage resources from the swarm members.We address the optimization of a
swarm-powered distributed 3D mapping mission for a real-life humanitarian
emergency response application through the exploitation of a swarm-powered ad
hoc cloud. Producing the relevant 3D maps in a timely manner, even when the
cloud connectivity is not available, is crucial to increase the chances of
success of the operation. In this work, we present a mathematical programming
heuristic based on decomposition and a variable neighborhood search heuristic
to minimize the completion time of the 3D reconstruction process necessary in
such missions. Our computational results reveal that the proposed heuristics
either quickly reach optimality or improve the best known solutions for almost
all tested realistic instances comprising up to 1000 images and fifteen drones.
