A Universal Framework for Extrinsic Calibration of Camera, Radar, and LiDAR
Sijie Hu, Alessandro Goldwurm, Martín Mujica, Sylvain Cadou, Frédéric Lerasle
Abstract
Accurate extrinsic calibration of camera, radar, and LiDAR is critical for multi-modal sensor fusion in autonomous vehicles and mobile robots. Existing methods typically perform pair-wise calibration and rely on specialized targets, limiting scalability and flexibility. We introduce a universal calibration framework based on an Iterative Best Match (IBM) algorithm that refines alignment by optimizing correspondences between sensors, eliminating traditional point-to-point matching. IBM naturally extends to simultaneous camera-LiDAR-radar calibration and leverages tracked natural targets (e.g., pedestrians) to establish cross-modal correspondences without predefined calibration markers. Experiments on a realistic multi-sensor platform (fisheye-camera, LiDAR, and radar) and the KITTI dataset validate the accuracy, robustness, and efficiency of our method.
BibTeX
@inproceedings{ral2026_auniversalframew,
title = {A Universal Framework for Extrinsic Calibration of Camera, Radar, and LiDAR},
author = {Sijie Hu and Alessandro Goldwurm and Martín Mujica and Sylvain Cadou and Frédéric Lerasle},
booktitle = {RA-L 2026},
year = {2026}
}