  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.
