无人机紧急降落时，用视觉识别危险区域并自动找安全着陆点。
Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments
- 通过语义分割实时评估视觉中的风险，生成像素级风险图。
- 在复杂城市环境中实现90%以上成功率，显著降低各类风险指标。
- 适合城市应急救援、快递配送等高风险场景下的无人机应用。
在拥挤的城市环境中，无人机紧急降落仍是关键挑战。本文提出一种基于视觉的风险感知方法，利用语义分割持续评估无人机视域内的潜在危险。通过专用深度神经网络为像素分配风险值，并基于风险地图算法，自适应识别受移动障碍物（如车辆、行人）和光照变化影响的稳定安全着陆区（SLZ）。控制系统据此引导无人机向低风险区域飞行，结合高度相关的安全阈值与时间上的着陆点稳定策略，确保下降轨迹稳健。在多种复杂城市环境中的实验验证表明，该方法在极具挑战的真实场景中实现了超过90%的着陆成功率，各项风险指标均有显著改善。结果表明，面向风险的视觉方法能有效降低紧急降落事故风险，尤其适用于充满移动危险障碍物的非结构化城市场景，极大提升无人机在复杂城市任务中的实际能力。
原文摘要 · Abstract (English)
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.
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