# Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration

arXiv: https://arxiv.org/abs/2202.13708 · HTML: https://mlsanity.com/paper/2202.13708/

## Key facts

- Cited by 16 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Feb 2022.

## Built on by (most cited first, 16 of 16)

- [OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving](https://mlsanity.com/paper/2205.14087/) (2022)
- [LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution](https://mlsanity.com/paper/2303.09825/) (2023)
- [Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching](https://mlsanity.com/paper/2404.18083/) (2024)
- [UniCal: Unified Neural Sensor Calibration](https://mlsanity.com/paper/2409.18953/) (2024)
- [Environment-Driven Online LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2502.00801/) (2025)
- [BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations](https://mlsanity.com/paper/2506.02587/) (2025)
- [End-to-End Lidar-Camera Self-Calibration for Autonomous Vehicles](https://mlsanity.com/paper/2304.12412/) (2023)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning](https://mlsanity.com/paper/2312.01085/) (2023)
- [UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations](https://mlsanity.com/paper/2504.01416/) (2025)
- [Cross-modal feature fusion for robust point cloud registration with ambiguous geometry](https://mlsanity.com/paper/2505.13088/) (2025)
- [One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving](https://mlsanity.com/paper/2511.12291/) (2025)
- [Online LiDAR-Camera Extrinsic Parameters Self-checking](https://mlsanity.com/paper/2210.10537/) (2022)
- [A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System](https://mlsanity.com/paper/2507.16621/) (2025)
- [LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement](https://mlsanity.com/paper/2509.25859/) (2025)
- [Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences](https://mlsanity.com/paper/2605.23397/) (2026)

## Abstract (arXiv)

Sensor-based environmental perception is a crucial step for autonomous driving systems, for which an accurate calibration between multiple sensors plays a critical role. For the calibration of LiDAR and camera, the existing method is generally to calibrate the intrinsic of the camera first and then calibrate the extrinsic of the LiDAR and camera. If the camera's intrinsic is not calibrated correctly in the first stage, it isn't easy to calibrate the LiDAR-camera extrinsic accurately. Due to the complex internal structure of the camera and the lack of an effective quantitative evaluation method for the camera's intrinsic calibration, in the actual calibration, the accuracy of extrinsic parameter calibration is often reduced due to the tiny error of the camera's intrinsic parameters. To this end, we propose a novel target-based joint calibration method of the camera intrinsic and LiDAR-camera extrinsic parameters. Firstly, we design a novel calibration board pattern, adding four circular holes around the checkerboard for locating the LiDAR pose. Subsequently, a cost function defined under the reprojection constraints of the checkerboard and circular holes features is designed to solve the camera's intrinsic parameters, distortion factor, and LiDAR-camera extrinsic parameter. In the end, quantitative and qualitative experiments are conducted in actual and simulated environments, and the result shows the proposed method can achieve accuracy and robustness performance. The open-source code is available at https://github.com/OpenCalib/JointCalib.