MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration

Mathieu Cocheteux, Julien Moreau, Franck Davoine
5/28/2024
cs.CV

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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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Cite this paper

@article{cocheteux2024muliev,
  title  = {MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration},
  author = {Mathieu Cocheteux and Julien Moreau and Franck Davoine},
  year   = {2024},
  eprint = {2405.18021},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.18021v1}
}

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