雷达-惯性里程计的等变滤波器
Equivariant Filter for Radar-Inertial Odometry
AI总结：
本文提出基于李群对称性的等变滤波器EqF，用于改进雷达-惯性里程计RIO，通过几何耦合导航状态与IMU偏置，提升鲁棒性和初始化精度。
摘要：
基于扩展卡尔曼滤波（EKF）的雷达-惯性里程计（RIO）依赖于雷达与惯性测量单元（IMU）之间的准确外校准，并对干扰敏感，因为较大的线性化误差可能会降低性能甚至导致发散。为了解决这些限制，本文提出了一种基于李群对称性的等变滤波器（EqF）用于RIO，通过几何耦合导航状态与IMU偏置，扩展以纳入雷达-IMU外校准和多状态约束更新。这种等变形式本质上保持了一致性并增强了鲁棒性，即使在较差或完全错误的校准状态初始化下也能实现可靠的态估计。在两种不同的无人 aerial vehicles（UAVs）上的实世界实验表明，所提出的EqF-RIO在正确的外校准下实现了最先进的准确性，并在较大的校准误差下提供了改进的收敛性，其中传统的EKF-RIO会失败。评估代码已开源。
英文摘要：
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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