TrajMatch: Toward Automatic Spatio-Temporal Calibration for Roadside LiDARs Through Trajectory Matching

Haojie Ren, Sha Zhang, Yao Li, Jianmin Ji, Yanyong Zhang

Abstract:

Recently, deploying sensors such as LiDARs on the
roadside to monitor the passing traffic and assist autonomous
vehicle perception has become popular. However, unlike
autonomous vehicle systems, roadside sensor systems involve
sensors from different subsystems, resulting in a lack of synchronization in both time and space between the sensors. Calibration
is a critical technology that enables the central server to fuse
data generated by different location infrastructures, which vastly
improves sensing range and detection robustness. Regrettably,
existing calibration algorithms frequently assume that LiDARs
have significant overlap or that temporal calibration has already
been achieved. However, since these assumptions do not always
hold in real-world scenarios, the calibration results obtained from
existing algorithms are frequently unsatisfactory. In this paper,
we propose TrajMatch - the first system that can automatically
calibrate 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, rather than relying on extracting
special features. Furthermore, we propose a novel mechanism for
evaluating calibration parameters that align with our algorithm,
and we demonstrate its effectiveness through experiments. This
mechanism can also guide parameter iterations for multiple
calibrations, further enhancing the accuracy and efficiency of
our calibration method. Finally, to evaluate the performance
of TrajMatch , we collected two datasets, one simulated dataset
LiDARnet-sim 1.0 and one real-world dataset. The experimental
results show that TrajMatch can achieve a spatial calibration
error of less than 10cm and a temporal calibration error of less
than 1.5ms.