This paper demonstrates how a heterogeneous fleet of unmanned aerial vehicles (UAVs) can support human operators in search and rescue (SaR) scenarios. We describe a fully autonomous delegation framework that interprets the top-level commands of the rescue team and converts them into actions of the UAVs. In particular, the UAVs are requested to autonomously scan a search area and to provide the operator with a consistent georeferenced 3D reconstruction of the environment to increase the environmental awareness and to support critical decision-making. The mission is executed based on the individual platform and sensor capabilities of rotary-and fixed-wing UAVs (RW-UAV and FW-UAV respectively): With the aid of an optical camera, the FW-UAV can generate a sparse point-cloud of a large area in a short amount of time. A LiDAR mounted on the autonomous helicopter is used to refine the visual point-cloud by generating denser point-clouds of specific areas of interest. In this context, we evaluate the performance of point-cloud registration methods to align two maps that were obtained by different sensors. In our validation, we compare classical point-cloud alignment methods to a novel probabilistic data association approach that specifically takes the individual point-cloud densities into consideration.