# Target-free Extrinsic Calibration of Event-LiDAR Dyad using Edge Correspondences

arXiv: https://arxiv.org/abs/2305.04017 · HTML: https://mlsanity.com/paper/2305.04017/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2023.

## Built on by (most cited first, 6 of 6)

- [MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration](https://mlsanity.com/paper/2405.18021/) (2024)
- [Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning](https://mlsanity.com/paper/2412.02119/) (2024)
- [One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving](https://mlsanity.com/paper/2511.12291/) (2025)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)
- [Temporal and Rotational Calibration for Event-Centric Multi-Sensor Systems](https://mlsanity.com/paper/2508.12564/) (2025)
- [LEAR: Learning Edge-Aware Representations for Event-to-LiDAR Localization](https://mlsanity.com/paper/2603.01839/) (2026)

## Abstract (arXiv)

Calibrating the extrinsic parameters of sensory devices is crucial for fusing multi-modal data. Recently, event cameras have emerged as a promising type of neuromorphic sensors, with many potential applications in fields such as mobile robotics and autonomous driving. When combined with LiDAR, they can provide more comprehensive information about the surrounding environment. Nonetheless, due to the distinctive representation of event cameras compared to traditional frame-based cameras, calibrating them with LiDAR presents a significant challenge. In this paper, we propose a novel method to calibrate the extrinsic parameters between a dyad of an event camera and a LiDAR without the need for a calibration board or other equipment. Our approach takes advantage of the fact that when an event camera is in motion, changes in reflectivity and geometric edges in the environment trigger numerous events, which can also be captured by LiDAR. Our proposed method leverages the edges extracted from events and point clouds and correlates them to estimate extrinsic parameters. Experimental results demonstrate that our proposed method is highly robust and effective in various scenes.