长距离四旋翼自主着陆的开发与测试
Development and Testing for Perception Based Autonomous Landing of a Long-Range QuadPlane
AI总结：
本文提出了一种轻量级四旋翼系统，用于高效的基于视觉的自主着陆和视觉惯性里程计，适用于长距离四旋翼任务。
摘要：
四旋翼结合固定翼飞机的航程效率和多旋翼平台的机动性，用于长距离自主任务。在GPS拒服或拥挤的城市环境中，基于感知的着陆对于可靠运行至关重要。与结构化的着陆区不同，现实世界中的地点是无结构的且高度多变的，要求感知系统具有强大的泛化能力。深度神经网络（DNNs）提供了一种可扩展的解决方案，用于在多样化的视觉和环境条件下学习着陆点特征。虽然基于感知的着陆已在模拟中展示，但实际部署带来了显著的挑战。载荷和体积限制限制了高性能边缘AI设备如NVIDIA Jetson Orin Nano的使用，这些设备对于实时检测和控制至关重要。在没有GPS的情况下，着陆下降期间的准确姿态估计是必要的，并依赖于可靠的视觉惯性里程计。在有限的边缘AI资源下实现这一点需要对整个部署框架进行仔细优化。大型四旋翼的飞行特性进一步复杂化了问题。这些飞机具有高惯性、减少的推力矢量和慢响应时间，进一步复杂了稳定的着陆操作。本文提出了一种轻量级的四旋翼系统，用于高效的基于视觉的自主着陆和视觉惯性里程计，特别为长距离四旋翼任务如空中监控而设计。它描述了硬件平台、传感器配置和嵌入式计算架构，以满足严格的时间和物理约束。这为在动态、无结构、GPS拒服环境中的自主着陆部署奠定了基础。
英文摘要：
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.
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。
