在线校准雷达与惯性传感器时间偏移，提升融合定位精度
EKF-Based Radar-Inertial Odometry with Online Temporal Calibration
- 用扩展卡尔曼滤波框架，将时间偏移作为状态变量在线估计
- 在真实数据集上实现毫秒级时间偏移估计，显著改善定位误差
- 适合需要高精度时序对齐的自动驾驶与机器人导航系统
异构传感器间精确的时间同步对多传感器融合系统的稳定状态估计至关重要。传感器延迟会导致事件实际发生时间与测量时间不一致，造成传感器数据流间的时序错位（时间偏移）。本文提出一种基于扩展卡尔曼滤波（EKF）的雷达-惯性里程计（RIO）框架，可在线估计时间偏移。通过将时间偏移引入单次雷达扫描得到的自身速度测量模型中，实现基于统一时间流的准确传播与更新。在仿真和真实数据集上的实验表明，该方法能精确估计时间偏移，并显著提升RIO性能，验证了传感器时间同步的重要性。代码已开源：https://github.com/spearwin/EKF-RIO-TC。
原文摘要 · Abstract (English)
Accurate time synchronization between heterogeneous sensors is crucial for ensuring robust state estimation in multi-sensor fusion systems. Sensor delays often cause discrepancies between the actual time when the event was captured and the time of sensor measurement, leading to temporal misalignment (time offset) between sensor measurement streams. In this paper, we propose an extended Kalman filter (EKF)-based radar-inertial odometry (RIO) framework that estimates the time offset online. The radar ego-velocity measurement model, derived from a single radar scan, is formulated to incorporate the time offset into the update. By leveraging temporal calibration, the proposed RIO enables accurate propagation and measurement updates based on a common time stream. Experiments on both simulated and real-world datasets demonstrate the accurate time offset estimation of the proposed method and its impact on RIO performance, validating the importance of sensor time synchronization. Our implementation of the EKF-RIO with online temporal calibration is available at https://github.com/spearwin/EKF-RIO-TC.
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