QuadPlanes combine the range efficiency of fixed-wing aircraft with the maneuverability of multi-rotor platforms for long-range autonomous missions. In GPS-denied or cluttered urban environments, perception-based landing is vital for reliable operation. Unlike structured landing zones, real-world sites are unstructured and highly variable, requiring strong generalization capabilities from the perception system. Deep neural networks (DNNs) provide a scalable solution for learning landing site features across diverse visual and environmental conditions. While perception-driven landing has been shown in simulation, real-world deployment introduces significant challenges. Payload and volume constraints limit high-performance edge AI devices like the NVIDIA Jetson Orin Nano, which are crucial for real-time detection and control. Accurate pose estimation during descent is necessary, especially in the absence of GPS, and relies on dependable visual-inertial odometry. Achieving this with limited edge AI resources requires careful optimization of the entire deployment framework. The flight characteristics of large QuadPlanes further complicate the problem. These aircraft exhibit high inertia, reduced thrust vectoring, and slow response times further complicate stable landing maneuvers. This work presents a lightweight QuadPlane system for efficient vision-based autonomous landing and visual-inertial odometry, specifically developed for long-range QuadPlane operations such as aerial monitoring. It describes the hardware platform, sensor configuration, and embedded computing architecture designed to meet demanding real-time, physical constraints. This establishes a foundation for deploying autonomous landing in dynamic, unstructured, GPS-denied environments.

翻译：四轴复合翼无人机融合了固定翼飞机的航程效率与多旋翼平台的机动性，适用于长航程自主任务。在GPS拒止或复杂城市环境中，基于感知的着陆对于可靠运行至关重要。与结构化着陆区不同，真实世界的着陆点通常为非结构化且高度多变，要求感知系统具备强大的泛化能力。深度神经网络为学习不同视觉与环境条件下的着陆点特征提供了可扩展的解决方案。尽管感知驱动的着陆已在仿真中得到验证，但实际部署面临显著挑战。有效载荷与空间限制制约了高性能边缘AI设备（如NVIDIA Jetson Orin Nano）的使用，而这些设备对实时检测与控制至关重要。在下降过程中，尤其是在缺乏GPS的情况下，精确的姿态估计依赖于可靠的视觉-惯性里程计。在边缘AI资源有限的情况下实现此目标，需要对整个部署框架进行精心优化。大型四轴复合翼无人机的飞行特性进一步加剧了问题的复杂性。这类飞行器具有高惯性、推力矢量控制能力减弱以及响应速度慢等特点，使得稳定着陆机动更为困难。本研究提出了一种轻量级四轴复合翼系统，用于实现高效的基于视觉的自主着陆与视觉-惯性里程计，专门为长航程四轴复合翼操作（如空中监测）而开发。文中详细介绍了为满足严苛的实时性与物理约束而设计的硬件平台、传感器配置及嵌入式计算架构。这为在动态、非结构化、GPS拒止环境中部署自主着陆技术奠定了基础。