面向飞行器的鲁棒4D雷达辅助惯性导航
Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles
AI总结：
针对无人机常用传感器在复杂环境失效的问题，提出基于ESKF的4D雷达-惯性紧耦合导航方法，结合点到分布扫描匹配与关键帧先验地图匹配，经实验验证精度和鲁棒性优于现有方法。
摘要：
尽管激光雷达和摄像头在无人机（UAV）中已十分普及，但在复杂环境中可能失效；而能够提供鲁棒3D测距与多普勒速度测量的4D毫米波（MMW）雷达在飞行器导航中的应用仍较少。本文提出了一种高效鲁棒的基于误差状态卡尔曼滤波（ESKF）的无人机雷达-惯性导航方法。该方法的核心思路是采用点到分布的雷达扫描匹配，提供带有合理不确定性量化的运动约束，这些约束与多普勒速度测量一起，以紧耦合方式更新导航状态。此外，我们提出了一种鲁棒的基于关键帧的匹配方案，可与先验地图（若可用）进行匹配以限制累积导航误差，从而提供高精度的基于雷达的全局定位解决方案。大量真实世界实验验证表明，所提出的雷达辅助惯性导航方法在精度和鲁棒性上均优于现有最优方法。
英文摘要：
While LiDAR and cameras are becoming ubiquitous for unmanned aerial vehicles (UAVs) but can be ineffective in challenging environments, 4D millimeter-wave (MMW) radars that can provide robust 3D ranging and Doppler velocity measurements are less exploited for aerial navigation. In this paper, we develop an efficient and robust error-state Kalman filter (ESKF)-based radar-inertial navigation for UAVs. The key idea of the proposed approach is the point-to-distribution radar scan matching to provide motion constraints with proper uncertainty qualification, which are used to update the navigation states in a tightly coupled manner, along with the Doppler velocity measurements. Moreover, we propose a robust keyframe-based matching scheme against the prior map (if available) to bound the accumulated navigation errors and thus provide a radar-based global localization solution with high accuracy. Extensive real-world experimental validations have demonstrated that the proposed radar-aided inertial navigation outperforms state-of-the-art methods in both accuracy and robustness.
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。
