合成到真实的着陆区安全检测流水线
Synthetic-to-Real Pipeline for Safe Landing Zone Detection
AI总结：
提出一种合成数据生成与感知流水线，通过域随机化生成逼真城市环境并微调Transformer架构，结合欧几里得距离变换实现无碰撞着陆区检测，消除手动标注需求。
摘要：
随着无人飞行器（UAV）向更高自主性过渡，在非合作、非结构化环境中执行无辅助回收的能力变得至关重要。实现安全自主着陆需要高保真语义分辨率以区分可通行地形与危险障碍物，然而开发常因标注航拍数据集的稀缺而受阻。本文提出一种全面的感知与数据生成流水线，旨在弥合自主着陆任务的模拟到现实差距。我们引入一个程序化合成数据引擎，通过域随机化生成具有自动语义标注的逼真城市环境。基于Transformer的OneFormer架构仅在此合成数据上微调，利用多头自注意力机制进行全局上下文解析。为确保操作安全，一个确定性着陆模块利用欧几里得距离变换（EDT）和动态推理逻辑，在障碍物周围保持严格安全缓冲的同时，识别最大的内接安全着陆区。针对UAVid数据集的定量基准测试展示了鲁棒的语义分割性能，而在真实世界无人机视频上的定性验证证实了系统在未见环境中识别无碰撞着陆点的能力。我们的结果凸显了高保真程序化模拟在消除手动标注需求的同时，为自主UAV回收提供鲁棒、边缘可部署的态势感知的潜力。
英文摘要：
As Uncrewed Aerial Vehicles (UAVs) transition toward higher levels of autonomy, the ability to perform unassisted recovery in non-cooperative, unstructured environments becomes critical. Achieving safe autonomous landing requires high-fidelity semantic resolution to distinguish navigable terrain from hazardous obstacles, yet development is often hindered by the scarcity of annotated aerial datasets. This work proposes a comprehensive perception and data generation pipeline designed to bridge the sim-to-real gap for autonomous landing tasks. We introduce a procedural synthetic data engine that generates photorealistic urban environments with automated semantic annotations through domain randomization. A Transformer-based OneFormer architecture is fine-tuned exclusively on this synthetic data, leveraging multi-head self-attention mechanisms for global context resolution. To ensure operational safety, a deterministic landing module utilizes a Euclidean Distance Transform (EDT) and dynamic inference logic to identify the largest inscribed safe landing zones while maintaining strict clearance buffers around obstacles. Quantitative benchmarking against the UAVid dataset demonstrates robust semantic segmentation performance, while qualitative validation on real-world UAV footage confirms the system's ability to identify collision-free landing sites in unseen environments. Our results highlight the potential of high-fidelity procedural simulation to eliminate the need for manual annotation while providing robust, edge-deployable situational awareness for autonomous UAV recovery.
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。
