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

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 -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, we collected two datasets, one simulated dataset and one real-world dataset. The experimental results show that can achieve a spatial calibration error of less than $10cm$ and a temporal calibration error of less than 1.5ms