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arXiv · 2405.18021

MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration

Mathieu Cocheteux·Julien Moreau·Franck Davoine
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Abstract

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.

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Mathieu Cocheteux, Julien Moreau, Franck Davoine. 2024-05-28. MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration. https://doi.org/10.1109/cvprw63382.2024.00460

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