实现雷达与惯性传感器的实时时空联合标定，提升恶劣环境下的定位精度。
Radar-Inertial Odometry with Online Spatio-Temporal Calibration via Continuous-Time IMU Modeling
- 基于连续时间建模的B样条方法，统一处理异步传感数据。
- 无需依赖特征匹配或特定环境假设，可同时估计时空参数。
- 适合自动驾驶、机器人在低光照或雾霾中的高鲁棒性定位场景。
雷达-惯性里程计（RIO）在低光、雾天、无纹理环境或恶劣天气下，已成为视觉和激光雷达里程计的可靠替代方案。然而，现有RIO方法通常假设雷达-惯性外部标定已知，或依赖充足运动激励进行在线外部标定，且常忽略传感器间的时序偏差。本文提出一种基于因子图优化框架的RIO系统，通过使用均匀三次B样条对惯性测量进行连续时间建模，实现空间与时间参数的联合在线标定。该连续时间表示能准确捕捉雷达与惯性数据的异步特性，使时间偏移和外部标定参数均可稳定收敛，且不依赖扫描匹配、目标跟踪或特定环境假设。
原文摘要 · Abstract (English)
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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