用4D雷达提升无人机在恶劣环境下的导航精度与鲁棒性
Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles
- 通过点对分布雷达匹配提供带不确定度的运动约束
- 紧耦合融合多普勒速度，实现厘米级定位精度
- 基于关键帧的匹配方案支持高精度全局定位，适合复杂场景
尽管激光雷达和摄像头在无人机中日益普及，但在恶劣环境下表现不佳。4D毫米波雷达能提供可靠的3D测距与多普勒速度信息，却未被充分用于空中导航。本文提出一种高效且鲁棒的基于误差状态卡尔曼滤波（ESKF）的雷达-惯性导航方法。核心思想是采用点对分布雷达扫描匹配，生成带有合理不确定性量化的位置约束，并与多普勒速度数据紧密耦合，实时更新导航状态。此外，提出一种基于关键帧的匹配机制，可与已有地图进行比对，有效抑制累积误差，实现高精度雷达辅助全局定位。大量真实世界实验验证表明，该方法在精度和鲁棒性上均优于现有先进方法。
原文摘要 · Abstract (English)
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.
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