MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration

Mathieu Cocheteux, Julien Moreau, Franck Davoine; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4579-4586

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 that are essential for maintaining optimal sensor alignment amidst varying operational conditions. Rigorously evaluated against the real-world scenarios presented 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. Our findings reveal the potential of MULi-Ev to bolster the safety reliability and overall performance of event-based perception systems in autonomous driving marking a significant step forward in their real-world deployment and effectiveness.