We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages the kinematic correlation of two sets of linear velocity estimates from event data and wheel odometers, respectively. The overall calibration task consists of estimating the underlying temporal offset between the two heterogeneous sensors, and furthermore, recovering the extrinsic rotation that defines the linear relationship between the two sets of velocity estimates. The first sub-problem is formulated as an optimization one, which looks for the optimal temporal offset that maximizes a correlation measurement invariant to arbitrary linear transformation. Once the temporal offset is compensated, the extrinsic rotation can be worked out with an iterative closed-form solver that incrementally registers associated linear velocity estimates. The proposed algorithm is proved effective on both synthetic data and real data, outperforming traditional methods based on alignment of trajectories.

翻译：我们针对安装在全向车辆上的事件相机的时空校准问题提出了一种解决方案。与传统方法通常利用轨迹对齐来确定相机相对于车辆车身坐标系的位姿不同，我们的方法利用了来自事件数据和车轮里程计的两组线速度估计之间的运动学相关性。整个校准任务包括估计两个异构传感器之间的时间偏移量，以及恢复定义两组速度估计之间线性关系的外参旋转矩阵。第一个子问题被表述为一个优化问题，即寻找最优时间偏移量，以最大化一个对任意线性变换具有不变性的相关度量。一旦时间偏移量得到补偿，可通过迭代闭式求解器逐步配准相关的线速度估计，从而解算出外参旋转矩阵。所提出的算法在合成数据和真实数据上均被验证有效，且性能优于基于轨迹对齐的传统方法。