雷达-惯性里程计与连续时间惯性测量模型的在线时空校准
Radar-Inertial Odometry with Online Spatio-Temporal Calibration via Continuous-Time IMU Modeling
AI总结：
本文提出基于连续时间惯性测量模型的雷达-惯性里程计框架，实现在线空间和时间校准，无需依赖扫描匹配或环境假设，提高鲁棒性。
摘要：
雷达-惯性里程计（RIO）已作为一种稳健的替代方案，用于在低光、雾、无特征环境或恶劣天气条件下，替代视觉和LiDAR里程计。然而，许多现有RIO方法假设已知雷达-惯性测量单元（IMU）的外校准或依赖足够的运动激励进行在线外校准，而传感器之间的时间偏移常被忽视或独立处理。本文提出一个RIO框架，基于连续时间建模的惯性测量，使用均匀三次B样条，在因子图优化公式中实现联合在线空间和时间校准。所提出的连续时间表示法准确捕捉雷达-IMU测量的异步特性，使时间偏移和外校准参数能够可靠收敛，而无需依赖扫描匹配、目标跟踪或环境特定假设。
英文摘要：
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.
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