Recently, it has become popular to deploy sensors such as LiDARs on the roadside to monitor the passing traffic and assist autonomous vehicle perception. Unlike autonomous vehicle systems, roadside sensors are usually affiliated with different subsystems and lack synchronization both in time and space. Calibration is a key technology which allows the central server to fuse the data generated by different location infrastructures, which can deliver improve the sensing range and detection robustness. Unfortunately, existing calibration algorithms often assume that the LiDARs are significantly overlapped or that the temporal calibration is already achieved. Since these assumptions do not always hold in the real world, the calibration results from the existing algorithms are often unsatisfactory and always need human involvement, which brings high labor costs. In this paper, we propose TrajMatch -- the first system that can automatically calibrate for roadside LiDARs in both time and space. The main idea is to automatically calibrate the sensors based on the result of the detection/tracking task instead of extracting special features. More deeply, we propose a mechanism for evaluating calibration parameters that is consistent with our algorithm, and we demonstrate the effectiveness of this scheme experimentally, which can also be used to guide parameter iterations for multiple calibration. Finally, to evaluate the performance of TrajMatch , we collect two dataset, one simulated dataset LiDARnet-sim 1.0 and a real-world dataset. Experiment results show that TrajMatch can achieve a spatial calibration error of less than 10cm and a temporal calibration error of less than 1.5ms.

翻译：近年来，在路侧部署激光雷达等传感器以监测通行交通并辅助自动驾驶车辆感知的做法日益普及。与自动驾驶车辆系统不同，路侧传感器通常隶属于不同子系统，且缺乏时间与空间上的同步。校准是一项关键技术，它能使中心服务器融合不同位置基础设施生成的数据，从而提升感知范围与检测鲁棒性。然而，现有校准算法通常假设激光雷达视场存在显著重叠，或时间校准已预先完成。由于这些假设在现实世界中并不总是成立，现有算法的校准结果往往不尽如人意，且常需人工介入，导致高昂的人力成本。本文提出TrajMatch——首套能够自动完成路侧激光雷达时空校准的系统。其核心思路是基于检测/跟踪任务的结果而非提取特殊特征进行自动传感器校准。更深层次地，我们提出了一种与算法一致的校准参数评估机制，并通过实验验证了该方案的有效性；该机制还可用于指导多传感器校准的参数迭代。最后，为评估TrajMatch性能，我们收集了两个数据集：模拟数据集LiDARnet-sim 1.0与真实世界数据集。实验结果表明，TrajMatch可实现小于10厘米的空间校准误差和小于1.5毫秒的时间校准误差。