# Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

arXiv: https://arxiv.org/abs/2101.04431 · HTML: https://mlsanity.com/paper/2101.04431/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [LiDAR-Camera Calibration using 3D-3D Point correspondences](https://mlsanity.com/paper/1705.09785/) (2017)
- [Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups](https://mlsanity.com/paper/1705.04085/) (2017)
- [Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences](https://mlsanity.com/paper/1904.12433/) (2019)
- [Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach](https://mlsanity.com/paper/2001.06175/) (2020)
- [A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces](https://mlsanity.com/paper/1904.12116/) (2019)
- [Towards Autonomous Driving: a Multi-Modal 360circ Perception Proposal](https://mlsanity.com/paper/2008.09672/) (2020)

## Built on by (most cited first, 25 of 25)

- [The GOOSE Dataset for Perception in Unstructured Environments](https://mlsanity.com/paper/2310.16788/) (2023)
- [DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic Exploration in the Dark](https://mlsanity.com/paper/2403.10814/) (2024)
- [Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentation](https://mlsanity.com/paper/2409.18788/) (2024)
- [Online,Target-Free LiDAR-Camera Extrinsic Calibration via Cross-Modal Mask Matching](https://mlsanity.com/paper/2404.18083/) (2024)
- [Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything](https://mlsanity.com/paper/2306.02656/) (2023)
- [CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network](https://mlsanity.com/paper/2311.15241/) (2023)
- [DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications](https://mlsanity.com/paper/2406.10068/) (2024)
- [UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception](https://mlsanity.com/paper/2604.14089/) (2026)
- [GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration](https://mlsanity.com/paper/2212.12745/) (2022)
- [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)
- [PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration](https://mlsanity.com/paper/2309.09855/) (2023)
- [SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles](https://mlsanity.com/paper/2404.18411/) (2024)
- [L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration](https://mlsanity.com/paper/2406.03298/) (2024)
- [USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots](https://mlsanity.com/paper/2304.01986/) (2023)
- [Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review](https://mlsanity.com/paper/2405.10132/) (2024)
- [Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization](https://mlsanity.com/paper/2406.11599/) (2024)
- [A Constrained Optimization Approach for Gaussian Splatting from Coarsely-posed Images and Noisy Lidar Point Clouds](https://mlsanity.com/paper/2504.09129/) (2025)
- [One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving](https://mlsanity.com/paper/2511.12291/) (2025)
- [Joint object detection and re-identification for 3D obstacle multi-camera systems](https://mlsanity.com/paper/2310.05785/) (2023)
- [A Complete System for Automated 3D Semantic-Geometric Mapping of Corrosion in Industrial Environments](https://mlsanity.com/paper/2404.13691/) (2024)
- [A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System](https://mlsanity.com/paper/2507.16621/) (2025)
- [RLCNet: An end-to-end deep learning framework for simultaneous online calibration of LiDAR, RADAR, and Camera](https://mlsanity.com/paper/2512.08262/) (2025)
- [Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2606.20103/) (2026)
- [PLS-Calib: A Partial Least Squares Framework for Event Camera and Odometry Calibration under Ground Motion Constraints](https://mlsanity.com/paper/2608.03296/) (2026)

## Abstract (arXiv)

Most sensor setups for onboard autonomous perception are composed of LiDARs and vision systems, as they provide complementary information that improves the reliability of the different algorithms necessary to obtain a robust scene understanding. However, the effective use of information from different sources requires an accurate calibration between the sensors involved, which usually implies a tedious and burdensome process. We present a method to calibrate the extrinsic parameters of any pair of sensors involving LiDARs, monocular or stereo cameras, of the same or different modalities. The procedure is composed of two stages: first, reference points belonging to a custom calibration target are extracted from the data provided by the sensors to be calibrated, and second, the optimal rigid transformation is found through the registration of both point sets. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups. In order to assess the performance of the proposed method, a novel evaluation suite built on top of a popular simulation framework is introduced. Experiments on the synthetic environment show that our calibration algorithm significantly outperforms existing methods, whereas real data tests corroborate the results obtained in the evaluation suite. Open-source code is available at https://github.com/beltransen/velo2cam_calibration