  Autonomous identification and evaluation of safe landing zones are of
paramount importance for ensuring the safety and effectiveness of aerial robots
in the event of system failures, low battery, or the successful completion of
specific tasks. In this paper, we present a novel approach for detection and
assessment of potential landing sites for safe quadrotor landing. Our solution
efficiently integrates 2D and 3D environmental information, eliminating the
need for external aids such as GPS and computationally intensive elevation
maps. The proposed pipeline combines semantic data derived from a Neural
Network (NN), to extract environmental features, with geometric data obtained
from a disparity map, to extract critical geometric attributes such as slope,
flatness, and roughness. We define several cost metrics based on these
attributes to evaluate safety, stability, and suitability of regions in the
environments and identify the most suitable landing area. Our approach runs in
real-time on quadrotors equipped with limited computational capabilities.
Experimental results conducted in diverse environments demonstrate that the
proposed method can effectively assess and identify suitable landing areas,
enabling the safe and autonomous landing of a quadrotor.
