# Automated Static Camera Calibration with Intelligent Vehicles

arXiv: https://arxiv.org/abs/2304.10814 · HTML: https://mlsanity.com/paper/2304.10814/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [2D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloud](https://mlsanity.com/paper/1904.09742/) (2019)
- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences](https://mlsanity.com/paper/2004.00740/) (2020)
- [Certifiably Optimal Monocular Hand-Eye Calibration](https://mlsanity.com/paper/2005.08298/) (2020)
- [Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions](https://mlsanity.com/paper/2101.11440/) (2021)
- [Extrinsic Camera Calibration with Semantic Segmentation](https://mlsanity.com/paper/2208.03949/) (2022)
- [Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions](https://mlsanity.com/paper/2201.04473/) (2022)

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

- [Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning](https://mlsanity.com/paper/2410.19788/) (2024)
- [MamV2XCalib: V2X-based Target-less Infrastructure Camera Calibration with State Space Model](https://mlsanity.com/paper/2507.23595/) (2025)

## Abstract (arXiv)

Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated geo-referenced camera calibration. Our method requires a calibration vehicle equipped with a combined GNSS/RTK receiver and an inertial measurement unit (IMU) for self-localization. In order to remove any requirements for the target's appearance and the local traffic conditions, we propose a novel approach using hypothesis filtering. Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle. Furthermore, we do not limit road access for other road users during calibration. We demonstrate the feasibility and accuracy of our approach by evaluating our approach on synthetic datasets as well as a real-world connected intersection, and deploying the calibration on real infrastructure. Our source code is publicly available.