基于高程图梯度的千米级GNSS拒止无人机导航：SPRIN-D挑战优胜系统
Kilometer-Scale GNSS-Denied UAV Navigation via Heightmap Gradients: A Winning System from the SPRIN-D Challenge
AI总结：
针对GNSS拒止环境下无人机长距离飞行中的漂移问题，提出一种利用高程图梯度模板匹配进行漂移校正的轻量级定位方法，并在SPRIN-D挑战中实现9公里航点导航。
摘要：
在GNSS拒止环境中实现可靠的长距离无人机飞行具有挑战性：集成里程计会导致漂移，在未探索区域无法进行闭环检测，且嵌入式平台计算能力有限。我们提出了一套完全机载的无人机系统，专为SPRIN-D Funke Fully Autonomous Flight Challenge开发，该挑战要求在没有GNSS或先验密集地图的情况下，在低于25米AGL（离地高度）的高度完成9公里长距离航点导航。该系统集成了感知、建图、规划和控制，并采用一种轻量级漂移校正方法，通过梯度模板匹配将激光雷达导出的局部高程图与先验地理数据高程图进行匹配，并在聚类粒子滤波器中融合里程计证据。在竞赛部署中，该系统在城区、森林和开阔地形中执行了千米级飞行，相对于原始里程计显著减少了漂移，同时在仅CPU硬件上实时运行。我们描述了系统架构、定位流程和竞赛评估，并报告了现场部署中的实际经验，为GNSS拒止无人机自主性的设计提供了参考。
英文摘要：
Reliable long-range flight of unmanned aerial vehicles (UAVs) in GNSS-denied environments is challenging: integrating odometry leads to drift, loop closures are unavailable in previously unseen areas and embedded platforms provide limited computational power. We present a fully onboard UAV system developed for the SPRIN-D Funke Fully Autonomous Flight Challenge, which required 9 km long-range waypoint navigation below 25 m AGL (Above Ground Level) without GNSS or prior dense mapping. The system integrates perception, mapping, planning, and control with a lightweight drift-correction method that matches LiDAR-derived local heightmaps to a prior geo-data heightmap via gradient-template matching and fuses the evidence with odometry in a clustered particle filter. Deployed during the competition, the system executed kilometer-scale flights across urban, forest, and open-field terrain and reduced drift substantially relative to raw odometry, while running in real time on CPU-only hardware. We describe the system architecture, the localization pipeline, and the competition evaluation, and we report practical insights from field deployment that inform the design of GNSS-denied UAV autonomy.
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。
