Radar-Inertial Odometry with Online Spatio-Temporal Calibration via Continuous-Time IMU Modeling

Abstract

Radar-Inertial Odometry (RIO) has emerged as a robust alternative to vision- and LiDAR-based odometry in challenging conditions such as low light, fog, featureless environments, or in adverse weather. However, many existing RIO approaches assume known radar-IMU extrinsic calibration or rely on sufficient motion excitation for online extrinsic estimation, while temporal misalignment between sensors is often neglected or treated independently. In this work, we present a RIO framework that performs joint online spatial and temporal calibration within a factor-graph optimization formulation, based on continuous-time modeling of inertial measurements using uniform cubic B-splines. The proposed continuous-time representation of acceleration and angular velocity accurately captures the asynchronous nature of radar-IMU measurements, enabling reliable convergence of both the temporal offset and extrinsic calibration parameters, without relying on scan matching, target tracking, or environment-specific assumptions.

Figures (4)

• Figure 1: Temporal misalignment between discrete radar ego-velocity measurements and the continuous-time motion estimated via a uniform cubic B-spline fit to IMU acceleration signals.
• Figure 2: Factor graph architecture employed in this work. The graph comprises an IMU factor ($f_{IMU}$), a radar ego-velocity factor ($f_{R}$), and a constant time-offset ($f_{CT}$) and constant extrinsic calibration factors ($f_{CE}$).
• Figure 3: Trajectory visualization of EKF-RIO with original and estimated parameters on Sequence 5 from EKF-RIO-TC dataset kim2025ekf.
• Figure 4: Temporal offset estimation results. (a) Convergence under various initial values. (b) Comparison of LC-RIO-ET, EKF-RIO-TC, and RIO-T.