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.

本文介绍了一种用于检测和评估四旋翼机器人安全降落潜在地点的新方法，该方法通过整合 2D 和 3D 环境信息，从神经网络 (NN) 获得语义数据以及从视差图获得几何信息，定义了基于几何属性的成本指标，以评估环境中区域的安全性、稳定性和适用性，并实时运行于计算能力有限的四旋翼机器人上，实验证明该方法能够有效评估和识别合适的降落区域，实现四旋翼机器人的安全自主降落。