# An Interactive LiDAR to Camera Calibration

arXiv: https://arxiv.org/abs/1903.02122 · HTML: https://mlsanity.com/paper/1903.02122/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [A Wearable Data Collection System for Studying Micro-Level E-Scooter Behavior in Naturalistic Road Environment](https://mlsanity.com/paper/2212.11979/) (2022)
- [CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map](https://mlsanity.com/paper/2110.10194/) (2021)
- [RLCNet: An end-to-end deep learning framework for simultaneous online calibration of LiDAR, RADAR, and Camera](https://mlsanity.com/paper/2512.08262/) (2025)

## Abstract (arXiv)

Recent progress in the automated driving system (ADS) and advanced driver assistant system (ADAS) has shown that the combined use of 3D light detection and ranging (LiDAR) and the camera is essential for an intelligent vehicle to perceive and understand its surroundings. LiDAR-camera fusion requires precise intrinsic and extrinsic calibrations between the sensors. However, due to the limitation of the calibration equipment and susceptibility to noise, algorithms in existing methods tend to fail in finding LiDAR-camera correspondences in long-range. In this paper, we introduced an interactive LiDAR to camera calibration toolbox to estimate the intrinsic and extrinsic transforms. This toolbox automatically detects the corner of a planer board from a sequence of LiDAR frames and provides a convenient user interface for annotating the corresponding pixels on camera frames. Since the toolbox only detects the top corner of the board, there is no need to prepare a precise polygon planar board or a checkerboard with different reflectivity areas as in the existing methods. Furthermore, the toolbox uses genetic algorithms to estimate the transforms and supports multiple camera models such as the pinhole camera model and the fisheye camera model. Experiments using Velodyne VLP-16 LiDAR and Point Grey Chameleon 3 camera show robust results.