基于视觉的风险感知紧急着陆：复杂城市环境中的无人机
Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments
AI总结：
本文提出一种风险感知方法，利用语义分割持续评估无人机视野中的潜在危险，通过深度神经网络分配像素级风险值，并应用风险地图算法，适应性地识别稳定着陆区，提升复杂城市环境中的紧急着陆安全性。
摘要：
在拥挤的城市环境中安全着陆仍然是无人机面临的关键挑战，尤其是在紧急情况下。本文提出了一种风险感知方法，利用语义分割持续评估无人机视野中的潜在危险。通过专用的深度神经网络分配像素级风险值，并应用基于风险地图的算法，我们的方法能够适应性地识别稳定安全着陆区（SLZ），即使存在移动关键障碍物如车辆、人群等及其他视觉挑战，如光照变化。随后，控制系统引导无人机向低风险区域移动，利用依赖高度的安全阈值和时间稳定着陆点，确保稳健的下降轨迹。在多样化的城市环境中进行的实验验证了该方法的有效性，实现了在非常具有挑战性的现实场景中超过90%的着陆成功率，显示出在各种风险指标上的显著改进。我们的发现表明，以风险为导向的视觉方法可以有效减少紧急着陆情况下的事故风险，特别是在复杂、非结构化的城市环境中，这些环境中密集分布着移动的高风险障碍物，同时增强了无人机在复杂城市操作中的真实能力。
英文摘要：
Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view. By using a specialized deep neural network to assign pixel-level risk values and applying an algorithm based on risk maps, our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles such as vehicles, people, etc., and other visual challenges like shifting illumination. A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization to ensure robust descent trajectories. Experimental validation in diverse urban environments demonstrates the effectiveness of our approach, achieving over 90% landing success rates in very challenging real scenarios, showing significant improvements in various risk metrics. Our findings suggest that risk-oriented vision methods can effectively help reduce the risk of accidents in emergency landing situations, particularly in complex, unstructured, urban scenarios, densely populated with moving risky obstacles, while potentiating the true capabilities of UAVs in complex urban operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
