Autonomous landing of uncrewed aerial vehicles (UAVs) in unknown, dynamic environments poses significant safety challenges, particularly near people and infrastructure, as UAVs transition to routine urban and rural operations. Existing methods often rely on prior maps, heavy sensors like LiDAR, static markers, or fail to handle non-cooperative dynamic obstacles like humans, limiting generalization and real-time performance. To address these challenges, we introduce SafeLand, a lean, vision-based system for safe autonomous landing (SAL) that requires no prior information and operates only with a camera and a lightweight height sensor. Our approach constructs an online semantic ground map via deep learning-based semantic segmentation, optimized for embedded deployment and trained on a consolidation of seven curated public aerial datasets (achieving 70.22% mIoU across 20 classes), which is further refined through Bayesian probabilistic filtering with temporal semantic decay to robustly identify metric-scale landing spots. A behavior tree then governs adaptive landing, iteratively validates the spot, and reacts in real time to dynamic obstacles by pausing, climbing, or rerouting to alternative spots, maximizing human safety. We extensively evaluate our method in 200 simulations and 60 end-to-end field tests across industrial, urban, and rural environments at altitudes up to 100m, demonstrating zero false negatives for human detection. Compared to the state of the art, SafeLand achieves sub-second response latency, substantially lower than previous methods, while maintaining a superior success rate of 95%. To facilitate further research in aerial robotics, we release SafeLand's segmentation model as a plug-and-play ROS package, available at https://github.com/markus-42/SafeLand.

翻译：无人机在未知动态环境中的自主着陆，特别是当无人机向常规城乡作业过渡时，在人员与基础设施附近面临着严峻的安全挑战。现有方法通常依赖先验地图、激光雷达等重型传感器、静态标记物，或无法处理如人类等非合作动态障碍物，限制了方法的泛化能力和实时性能。为解决这些挑战，我们提出了SafeLand，一种轻量级的、基于视觉的安全自主着陆系统，该系统无需先验信息，仅需一个摄像头和一个轻量级高度传感器即可运行。我们的方法通过基于深度学习的语义分割在线构建语义地面图，该分割模型针对嵌入式部署进行了优化，并在整合的七个精选公共航空数据集上训练（在20个类别上达到70.22% mIoU）。该语义图通过结合时间语义衰减的贝叶斯概率滤波进一步优化，以鲁棒地识别公制尺度的着陆点。随后，一个行为树控制自适应着陆过程，迭代验证着陆点，并通过暂停、爬升或重路由至备选着陆点等方式实时响应动态障碍物，最大限度地保障人员安全。我们在高达100米的高度下，于工业、城市和乡村环境中进行了200次仿真和60次端到端实地测试，广泛评估了我们的方法，实现了对人类检测的零漏报率。与现有技术相比，SafeLand实现了亚秒级的响应延迟，远低于先前方法，同时保持了95%的优异成功率。为促进空中机器人领域的进一步研究，我们将SafeLand的分割模型作为即插即用的ROS软件包发布，可在 https://github.com/markus-42/SafeLand 获取。