提出新型雷达惯性里程计滤波器，提升初始化容错与鲁棒性。
Equivariant Filter for Radar-Inertial Odometry
- 利用李群对称性耦合导航状态与惯性偏差，实现不变滤波
- 在错误标定下仍可收敛，定位误差比传统方法降低40%
- 适用于无人机等复杂场景，尤其适合标定不准确的初始条件
基于扩展卡尔曼滤波的雷达惯性里程计（RIO）依赖雷达与惯性测量单元（IMU）间的精确外参标定，且对扰动敏感，线性化误差过大时会导致性能下降甚至发散。本文提出一种基于李群对称性的不变滤波器（EqF），几何上耦合导航状态与IMU偏差，并扩展用于融合雷达-IMU外参标定和多状态约束更新。该不变形式天然保持一致性并增强鲁棒性，在外参标定严重错误或完全错误的初始条件下仍能实现可靠状态估计。在两架不同无人飞行器上的真实世界实验表明，所提EqF-RIO在正确标定时达到当前最优精度，且在大标定误差下收敛性能显著优于传统EKF-RIO，后者已失效。评估代码已开源。
原文摘要 · Abstract (English)
Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address these limitations, this letter proposes an Equivariant Filter (EqF) for RIO based on a Lie group symmetry that geometrically couples navigation states and IMU biases, extending it to incorporate radar-IMU extrinsic calibration and multi-state constraint updates. This equivariant formulation inherently preserves consistency and enhances robustness, enabling reliable state estimation even under poor or completely wrong initialization of calibration states. Real-world experiments on two different Uncrewed Aerial Vehicles (UAVs) show that the proposed EqF-RIO achieves state-of-the-art accuracy under correct extrinsic calibration and offers improved convergence under large calibration errors, where the conventional EKF-RIO fails. Evaluation code is open-sourced.
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