# TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

arXiv: https://arxiv.org/abs/2302.02157 · HTML: https://mlsanity.com/paper/2302.02157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 4 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [The Perfect Match: 3D Point Cloud Matching with Smoothed Densities](https://mlsanity.com/paper/1811.06879/) (2018)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics](https://mlsanity.com/paper/2008.08063/) (2020)
- [Online Temporal Calibration for Monocular Visual-Inertial Systems](https://mlsanity.com/paper/1808.00692/) (2018)
- [Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library](https://mlsanity.com/paper/2211.15975/) (2022)
- [Automatic Calibration of Dual-LiDARs Using Two Poles Stickered with Retro-Reflective Tape](https://mlsanity.com/paper/1911.00635/) (2019)

## Built on by (most cited first, 2 of 2)

- [Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review](https://mlsanity.com/paper/2405.10132/) (2024)
- [Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey](https://mlsanity.com/paper/2505.00747/) (2025)

## Abstract (arXiv)

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.