# Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems

arXiv: https://arxiv.org/abs/1904.08743 · HTML: https://mlsanity.com/paper/1904.08743/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network](https://mlsanity.com/paper/2505.22427/) (2025)
- [Edge-Aided Sensor Data Sharing in Vehicular Communication Networks](https://mlsanity.com/paper/2206.08882/) (2022)
- [SensorX2car: Sensors-to-car calibration for autonomous driving in road scenarios](https://mlsanity.com/paper/2301.07279/) (2023)
- [MamV2XCalib: V2X-based Target-less Infrastructure Camera Calibration with State Space Model](https://mlsanity.com/paper/2507.23595/) (2025)
- [Multi-task Learning for Camera Calibration](https://mlsanity.com/paper/2211.12432/) (2022)
- [Automated Automotive Radar Calibration With Intelligent Vehicles](https://mlsanity.com/paper/2306.13323/) (2023)
- [Radar-Camera Fused Multi-Object Tracking: Online Calibration and Common Feature](https://mlsanity.com/paper/2510.20794/) (2025)
- [calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments](https://mlsanity.com/paper/2603.06670/) (2026)
- [CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning](https://mlsanity.com/paper/2603.15767/) (2026)

## Abstract (arXiv)

Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception. The calibration of these heterogeneous sensor types in an automatic fashion during system operation is challenging due to differing physical measurement principles and the high sparsity of traffic radars. We propose - to the best of our knowledge - the first data-driven method for automatic rotational radar-camera calibration without dedicated calibration targets. Our approach is based on a coarse and a fine convolutional neural network. We employ a boosting-inspired training algorithm, where we train the fine network on the residual error of the coarse network. Due to the unavailability of public datasets combining radar and camera measurements, we recorded our own real-world data. We demonstrate that our method is able to reach precise and robust sensor registration and show its generalization capabilities to different sensor alignments and perspectives.