EKF-Based Radar-Inertial Odometry with Online Temporal Calibration
Changseung Kim, Geunsik Bae, Woojae Shin, Sen Wang, Hyondong Oh
AI summary
Problem
Heterogeneous sensors like radar and IMU suffer from inherent hardware and processing delays, causing temporal misalignment that degrades multi-sensor fusion accuracy. Existing radar-inertial odometry systems often ignore these offsets or rely on unavailable hardware triggers.
Approach
The method integrates a time offset variable into an extended Kalman filter and estimates it in real-time by comparing measured radar ego-velocity with predicted velocity, aligning both sensor streams to a common time reference.
Key results
- Real-time estimation of IMU-radar time offset using single-scan ego-velocity
- Validated accuracy across simulated and real-world datasets with and without hardware triggers
- Demonstrated significant RIO performance improvement when compensating for temporal misalignment
- Open-sourced implementation for seamless integration into existing radar-inertial frameworks
Why it matters
Enables robust, trigger-free sensor synchronization for reliable autonomous navigation in GPS-denied or adverse weather conditions.
Abstract
Accurate time synchronization between heteroge- neous sensors is crucial for ensuring robust state estimation in multi-sensor fusion systems. Sensor delays often cause discrepan- cies 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.