基于高度图梯度匹配实现无卫星信号下千米级无人机自主导航
Kilometer-Scale GNSS-Denied UAV Navigation via Heightmap Gradients: A Winning System from the SPRIN-D Challenge
- 用激光雷达局部高度图与先验地理高程图做梯度模板匹配
- 在真实城市/森林/空旷地飞行9公里，定位漂移显著降低
- 全程仅用CPU，适合资源受限的嵌入式无人系统
在无全球导航卫星系统（GNSS）环境下实现无人飞行器（UAV）的可靠长距离飞行极具挑战：里程计易产生漂移，未见过区域无法进行回环检测，且嵌入式平台算力有限。本文提出一种全机载无人机系统，用于应对SPRIN-D Funke完全自主飞行挑战，要求在无GNSS、无预先密集地图的情况下，以低于25米海拔（AGL）完成9公里长距离航点导航。该系统融合感知、建图、规划与控制，采用轻量级漂移校正方法，通过梯度模板匹配将激光雷达生成的局部高度图与先验地理数据高度图对齐，并在聚类粒子滤波器中融合该信息与里程计数据。竞赛部署中，系统成功在城市、森林和开阔地带执行千米级飞行任务，相比原始里程计大幅减少漂移，且仅依赖纯CPU硬件实时运行。本文详述系统架构、定位流程及竞赛评估结果，并分享现场部署的实用经验，为无卫星信号下的无人机自主飞行设计提供关键参考。
原文摘要 · Abstract (English)
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.
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