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.

翻译：基于标记的降落因其简单性和可靠性，在无人机配送与返航系统中得到广泛应用。然而，大多数方法假设理想的降落场地可见性与传感器性能，限制了其在复杂城市环境中的鲁棒性。我们在AirSim平台上提出了一套基于仿真的评估套件，通过系统性地变化城市布局、光照与天气条件，以复现真实的操作多样性。利用机载摄像头传感器（RGB用于标记检测，深度用于避障），我们对两种启发式覆盖模式与一个基于强化学习的智能体进行了基准测试，分析了探索策略与场景复杂度如何影响成功率、路径效率及鲁棒性。结果强调，需要在多样化且与传感器相关的条件下评估基于标记的自主降落，以指导可靠空中导航系统的开发。