Radar-Inertial Odometry with Online Spatio-Temporal Calibration via Continuous-Time IMU Modeling
This work addresses sensor calibration challenges for radar-inertial odometry systems in adverse conditions, representing an incremental improvement over existing methods.
The paper tackled the problem of radar-inertial odometry by developing a framework that jointly calibrates spatial and temporal sensor misalignments online, using continuous-time IMU modeling, which improved calibration convergence without relying on scan matching or environment-specific assumptions.
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.