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：我们针对全向车载事件相机的时空标定问题提出了一种解决方案。与传统方法通常通过轨迹对齐来确定相机相对于车体坐标系的位姿不同，我们的方法利用事件数据和轮式里程计分别估计的两组线速度之间的运动学关联。整体标定任务包括估计两个异构传感器之间的时间偏移，并进一步恢复刻画两组速度估计之间线性关系的外参旋转矩阵。第一个子问题被建模为优化问题，即寻找能使对任意线性变换不变的关联度量最大化的最优时间偏移。一旦时间偏移被补偿，外参旋转便可通过一种增量式配准关联线速度估计的迭代闭式求解器计算得出。所提出的算法在合成数据和真实数据上均证明有效，且优于基于轨迹对齐的传统方法。