  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.
