HUNT：通过瞬时相对帧在非结构化环境中进行高速无人机导航与跟踪
HUNT: High-Speed UAV Navigation and Tracking in Unstructured Environments via Instantaneous Relative Frames
AI总结：
提出HUNT框架，利用瞬时相对帧统一搜索与跟踪，实现高速飞行和鲁棒自主性。
摘要：
搜索与救援任务要求无人机既能高速穿越未知的非结构化环境，又能在检测到目标后跟踪目标。在感知退化且无全局定位的情况下实现这两种能力仍是一个开放挑战。最近的相对导航工作通过将规划和控制锚定到可见的检测目标上展示了鲁棒跟踪，但在视野中没有目标时无法进行导航。我们提出了HUNT（高速无人机导航与跟踪），一个实时框架，在单一相对公式中统一了穿越、获取和跟踪。HUNT直接从机载瞬时观测量（如姿态、高度和速度）定义导航目标，从而在搜索过程中实现反应式高速飞行。一旦检测到目标，相同的感知-控制管道无缝过渡到跟踪。在茂密森林、集装箱场地以及使用车辆和人体模型的搜索与救援任务中的户外实验表明，在全局方法失败的情况下，该框架实现了鲁棒自主性。
英文摘要：
Search and rescue operations require unmanned aerial vehicles to both traverse unknown unstructured environments at high speed and track targets once detected. Achieving both capabilities under degraded sensing and without global localization remains an open challenge. Recent works on relative navigation have shown robust tracking by anchoring planning and control to a visible detected object, but cannot address navigation when no target is in the field of view. We present HUNT (High-speed UAV Navigation and Tracking), a real-time framework that unifies traversal, acquisition, and tracking within a single relative formulation. HUNT defines navigation objectives directly from onboard instantaneous observables such as attitude, altitude, and velocity, enabling reactive high-speed flight during search. Once a target is detected, the same perception-control pipeline transitions seamlessly to tracking. Outdoor experiments in dense forests, container compounds, and search-and-rescue operations with vehicles and mannequins demonstrate robust autonomy where global methods fail.
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。
