面向异质城市环境的自主无人机着陆视觉标记搜索
Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments
AI总结：
本文提出基于模拟的评估套件，研究异质城市环境中自主无人机着陆的视觉标记搜索问题，通过对比启发式策略与强化学习方法，探讨复杂场景对着陆性能的影响。
摘要：
基于标记的着陆在无人机送货和返航系统中被广泛使用，因其简单性和可靠性。然而，大多数方法假设理想的着陆场地可见性和传感器性能，限制了在复杂城市环境中的鲁棒性。我们提出了一个基于模拟的评估套件，在AirSim平台上系统地变化了城市布局、光照和天气，以复制现实的运营多样性。使用机载摄像头传感器（RGB用于标记检测和深度用于障碍规避），我们对两种启发式覆盖模式和基于强化学习的智能体进行了基准测试，分析探索策略和场景复杂性对成功率、路径效率和鲁棒性的影响。结果强调了在多样、传感器相关的条件下评估基于标记的自主着陆系统的重要性，以指导可靠空中导航系统的发展。
英文摘要：
Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibility and sensor performance, limiting robustness in complex urban settings. We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity. Using onboard camera sensors (RGB for marker detection and depth for obstacle avoidance), we benchmark two heuristic coverage patterns and a reinforcement learning-based agent, analyzing how exploration strategy and scene complexity affect success rate, path efficiency, and robustness. Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial navigation systems.
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