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.

翻译：雷达-惯性里程计在低光照、雾霾、无特征环境或恶劣天气等挑战性条件下，已成为视觉和激光雷达里程计的鲁棒替代方案。然而，现有许多RIO方法假设已知雷达-IMU外参标定，或依赖充分运动激励实现在线外参估计，而传感器间的时间失配常被忽略或独立处理。本文提出一种基于均匀三次B样条对惯性测量进行连续时间建模的RIO框架，在因子图优化框架中实现联合在线空间与时间标定。所提出的加速度与角速度连续时间表示能够精确捕捉雷达-IMU测量的异步特性，无需依赖扫描匹配、目标跟踪或环境特定假设即可实现时间偏移与外参标定参数的可靠收敛。