基于视觉的无人机在人口密集区域的安全着陆：虚拟环境中的实时验证
Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments
AI总结：
本文提出利用Unreal和AirSim构建照片级逼真虚拟环境，实时评估基于视觉检测安全着陆区的无人机自主着陆策略，比较两种SLZ选择准则，并通过大量随机实验证明该算法能有效减少城市人口密集区域着陆事故。
摘要：
无人机（UAV）在人口密集区域的安全自主着陆是成功实现城市部署的关键环节，尤其是在紧急着陆情况下。然而，在真实场景中验证自主着陆是一项极具挑战性的任务，涉及伤害人员的高风险。在这项工作中，我们提出了一个框架，利用照片级逼真的虚拟环境，对人口密集场景中基于视觉的自主着陆进行实时、安全和全面的评估。我们提出使用Unreal图形引擎与AirSim插件相结合进行无人机仿真，并评估基于视觉检测安全着陆区（SLZ）的自主着陆策略在人口密集场景中的表现。然后，我们研究了两种不同的“最佳”SLZ选择准则，并在虚拟无人机于不同场景和条件下、城市场景中人员分布不同（包括移动人员）的情况下进行自主着陆时对它们进行了评估。我们评估了不同的指标来量化着陆策略的性能，为这一具有挑战性任务中的未来工作建立了比较基准，并通过大量随机迭代对这些指标进行了分析。研究表明，使用自主着陆算法能显著帮助防止涉及人员的事故，这可能有助于充分释放无人机在靠近人群的城市环境中的潜力。
英文摘要：
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.
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。
