  Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated
areas is a crucial aspect for successful urban deployment, particularly in
emergency landing situations. Nonetheless, validating autonomous landing in
real scenarios is a challenging task involving a high risk of injuring people.
In this work, we propose a framework for real-time safe and thorough evaluation
of vision-based autonomous landing in populated scenarios, using
photo-realistic virtual environments. We propose to use the Unreal graphics
engine coupled with the AirSim plugin for drone's simulation, and evaluate
autonomous landing strategies based on visual detection of Safe Landing Zones
(SLZ) in populated scenarios. Then, we study two different criteria for
selecting the "best" SLZ, and evaluate them during autonomous landing of a
virtual drone in different scenarios and conditions, under different
distributions of people in urban scenes, including moving people. We evaluate
different metrics to quantify the performance of the landing strategies,
establishing a baseline for comparison with future works in this challenging
task, and analyze them through an important number of randomized iterations.
The study suggests that the use of the autonomous landing algorithms
considerably helps to prevent accidents involving humans, which may allow to
unleash the full potential of drones in urban environments near to people.
