在复杂城市环境中评估无人机自主着陆的视觉标记搜索方法
Visual Marker Search for Autonomous Drone Landing in Diverse Urban Environments
- 用AirSim模拟不同城市布局、光照和天气下的着陆场景
- 对比启发式路径与强化学习策略，成功率受场景复杂度影响显著
- 为真实飞行系统开发提供可量化的评估基准，适合导航算法研究者
基于标记的着陆广泛应用于无人机配送和返航系统，因其简单可靠而被采用。然而，现有方法多假设理想化着陆区域可见性和传感器性能，限制了在复杂城市环境中的鲁棒性。本文基于AirSim平台构建仿真评估体系，系统性地改变城市布局、光照条件和天气状况，以模拟真实操作多样性。利用机载摄像头（RGB用于标记检测，深度相机用于避障），我们对比两种启发式覆盖模式与一个强化学习代理，分析探索策略与场景复杂度对成功率、路径效率及鲁棒性的影响。结果强调，必须在多样且与传感器相关的条件下评估基于标记的自主着陆，才能指导可靠空中导航系统的开发。
原文摘要 · Abstract (English)
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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