# Targetless 6DoF Calibration of LiDAR and 2D Scanning Radar Based on Cylindrical Occupancy

arXiv: https://arxiv.org/abs/2503.17002 · HTML: https://mlsanity.com/paper/2503.17002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://mlsanity.com/paper/2003.14032/) (2020)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Boreas: A Multi-Season Autonomous Driving Dataset](https://mlsanity.com/paper/2203.10168/) (2022)
- [Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection](https://mlsanity.com/paper/2306.01438/) (2023)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving](https://mlsanity.com/paper/2205.14087/) (2022)
- [SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera](https://mlsanity.com/paper/2003.04260/) (2020)
- [PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection](https://mlsanity.com/paper/2308.03982/) (2023)
- [iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems](https://mlsanity.com/paper/2407.11420/) (2024)
- [Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard](https://mlsanity.com/paper/1708.05514/) (2017)
- [Improving Extrinsics between RADAR and LIDAR using Learning](https://mlsanity.com/paper/2305.10594/) (2023)
- [Imaging radar and LiDAR image translation for 3-DOF extrinsic calibration](https://mlsanity.com/paper/2403.18358/) (2024)

## Abstract (arXiv)

Owing to the capability for reliable and all-weather long-range sensing, the fusion of LiDAR and Radar has been widely applied to autonomous vehicles for robust perception. In practical operation, well manually calibrated extrinsic parameters, which are crucial for the fusion of multi-modal sensors, may drift due to the vibration. To address this issue, we present a novel targetless calibration approach, termed LiRaCo, for the extrinsic 6DoF calibration of LiDAR and Radar sensors. Although both types of sensors can obtain geometric information, bridging the geometric correspondences between multi-modal data without any clues of explicit artificial markers is nontrivial, mainly due to the low vertical resolution of scanning Radar. To achieve the targetless calibration, LiRaCo leverages a spatial occupancy consistency between LiDAR point clouds and Radar scans in a common cylindrical representation, considering the increasing data sparsity with distance for both sensors. Specifically, LiRaCo expands the valid Radar scanned pixels into 3D occupancy grids to constrain LiDAR point clouds based on spatial consistency. Consequently, a cost function involving extrinsic calibration parameters is formulated based on the spatial overlap of 3D grids and LiDAR points. Extrinsic parameters are finally estimated by optimizing the cost function. Comprehensive quantitative and qualitative experiments on two real outdoor datasets with different LiDAR sensors demonstrate the feasibility and accuracy of the proposed method. The source code will be publicly available.