# Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information

arXiv: https://arxiv.org/abs/1902.10586 · HTML: https://mlsanity.com/paper/1902.10586/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

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

- [TEScalib: Targetless Extrinsic Self-Calibration of LiDAR and Stereo Camera for Automated Driving Vehicles with Uncertainty Analysis](https://mlsanity.com/paper/2202.13847/) (2022)
- [SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving](https://mlsanity.com/paper/2304.05530/) (2023)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [Camera Calibration via Circular Patterns: A Comprehensive Framework with Detection Uncertainty and Unbiased Projection Model](https://mlsanity.com/paper/2506.16842/) (2025)
- [AR Mapping: Accurate and Efficient Mapping for Augmented Reality](https://mlsanity.com/paper/2103.14846/) (2021)
- [Calousel: Extrinsic Calibration of Non-overlapping Multi-camera Systems from Pure Rotation](https://mlsanity.com/paper/2606.25646/) (2026)

## Abstract (arXiv)

This paper presents a framework for the targetless extrinsic calibration of stereo cameras and Light Detection and Ranging (LiDAR) sensors with a non-overlapping Field of View (FOV). In order to solve the extrinsic calibrations problem under such challenging configuration, the proposed solution exploits road markings as static and robust features among the various dynamic objects that are present in urban environment. First, this study utilizes road markings that are commonly captured by the two sensor modalities to select informative images for estimating the extrinsic parameters. In order to accomplish stable optimization, multiple cost functions are defined, including Normalized Information Distance (NID), edge alignment and, plane fitting cost. Therefore a smooth cost curve is formed for global optimization to prevent convergence to the local optimal point. We further evaluate each cost function by examining parameter sensitivity near the optimal point. Another key characteristic of extrinsic calibration, repeatability, is analyzed by conducting the proposed method multiple times with varying randomly perturbed initial points.