Visualization of MULi-Ev calibration results across different driving environments
Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR — two sensors pivotal in capturing comprehensive environmental information — remains unexplored.
We introduce MULi-Ev, the first online deep learning-based framework tailored for the extrinsic calibration of event cameras with LiDAR. This advancement is instrumental for the seamless integration of LiDAR and event cameras, enabling dynamic real-time calibration adjustments essential for maintaining optimal sensor alignment amidst varying operational conditions.
Rigorously evaluated against real-world scenarios in the DSEC dataset, MULi-Ev not only achieves substantial improvements in calibration accuracy but also sets a new standard for integrating LiDAR with event cameras in mobile platforms.
@InProceedings{Cocheteux_2024_CVPR,
author = {Cocheteux, Mathieu and Moreau, Julien and Davoine, Franck},
title = {MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2024},
pages = {4579-4586}
}