# LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution

arXiv: https://arxiv.org/abs/2303.09825 · HTML: https://mlsanity.com/paper/2303.09825/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package](https://mlsanity.com/paper/2109.07982/) (2021)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [Comparing Representations in Tracking for Event Camera-based SLAM](https://mlsanity.com/paper/2104.09887/) (2021)
- [Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2202.13708/) (2022)
- [Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences](https://mlsanity.com/paper/1904.12433/) (2019)
- [FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms](https://mlsanity.com/paper/2208.11865/) (2022)
- [Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration](https://mlsanity.com/paper/2010.14294/) (2020)
- [Improvements to Target-Based 3D LiDAR to Camera Calibration](https://mlsanity.com/paper/1910.03126/) (2019)

## Built on by (most cited first, 12 of 12)

- [MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration](https://mlsanity.com/paper/2405.18021/) (2024)
- [Environment-Driven Online LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2502.00801/) (2025)
- [CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement](https://mlsanity.com/paper/2502.17648/) (2025)
- [Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments](https://mlsanity.com/paper/2412.00291/) (2024)
- [EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera](https://mlsanity.com/paper/2409.19305/) (2024)
- [EI-Nexus: Towards Unmediated and Flexible Inter-Modality Local Feature Extraction and Matching for Event-Image Data](https://mlsanity.com/paper/2410.21743/) (2024)
- [One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving](https://mlsanity.com/paper/2511.12291/) (2025)
- [Event-Based Method for High-Speed 3D Deformation Measurement under Extreme Illumination Conditions](https://mlsanity.com/paper/2603.28159/) (2026)
- [DELTA: Dense Depth from Events and LiDAR using Transformer's Attention](https://mlsanity.com/paper/2505.02593/) (2025)
- [Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching](https://mlsanity.com/paper/2506.22784/) (2025)
- [Temporal and Rotational Calibration for Event-Centric Multi-Sensor Systems](https://mlsanity.com/paper/2508.12564/) (2025)
- [eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems](https://mlsanity.com/paper/2509.05923/) (2025)

## Abstract (arXiv)

The combination of LiDARs and cameras enables a mobile robot to perceive environments with multi-modal data, becoming a key factor in achieving robust perception. Traditional frame cameras are sensitive to changing illumination conditions, motivating us to introduce novel event cameras to make LiDAR-camera fusion more complete and robust. However, to jointly exploit these sensors, the challenging extrinsic calibration problem should be addressed. This paper proposes an automatic checkerboard-based approach to calibrate extrinsics between a LiDAR and a frame/event camera, where four contributions are presented. Firstly, we present an automatic feature extraction and checkerboard tracking method from LiDAR's point clouds. Secondly, we reconstruct realistic frame images from event streams, applying traditional corner detectors to event cameras. Thirdly, we propose an initialization-refinement procedure to estimate extrinsics using point-to-plane and point-to-line constraints in a coarse-to-fine manner. Fourthly, we introduce a unified and globally optimal solution to address two optimization problems in calibration. Our approach has been validated with extensive experiments on 19 simulated and real-world datasets and outperforms the state-of-the-art.