# Improving Extrinsics between RADAR and LIDAR using Learning

arXiv: https://arxiv.org/abs/2305.10594 · HTML: https://mlsanity.com/paper/2305.10594/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [Boreas: A Multi-Season Autonomous Driving Dataset](https://mlsanity.com/paper/2203.10168/) (2022)
- [Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments](https://mlsanity.com/paper/2109.13494/) (2021)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations](https://mlsanity.com/paper/2005.01456/) (2020)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar](https://mlsanity.com/paper/2004.03451/) (2020)
- [OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving](https://mlsanity.com/paper/2205.14087/) (2022)
- [Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar](https://mlsanity.com/paper/2001.10789/) (2020)
- [SOIC: Semantic Online Initialization and Calibration for LiDAR and Camera](https://mlsanity.com/paper/2003.04260/) (2020)
- [CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks](https://mlsanity.com/paper/2103.14793/) (2021)
- [Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning](https://mlsanity.com/paper/2105.14152/) (2021)
- [SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator](https://mlsanity.com/paper/2109.10270/) (2021)
- [Contrastive Learning of Features between Images and LiDAR](https://mlsanity.com/paper/2206.12071/) (2022)

## Built on by (most cited first, 2 of 2)

- [ROSS: Radar Off-road Semantic Segmentation](https://mlsanity.com/paper/2310.13551/) (2023)
- [Targetless 6DoF Calibration of LiDAR and 2D Scanning Radar Based on Cylindrical Occupancy](https://mlsanity.com/paper/2503.17002/) (2025)

## Abstract (arXiv)

LIDAR and RADAR are two commonly used sensors in autonomous driving systems. The extrinsic calibration between the two is crucial for effective sensor fusion. The challenge arises due to the low accuracy and sparse information in RADAR measurements. This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems. The method employs simple targets to generate data, including correspondence registration and a one-step optimization algorithm. The optimization aims to minimize the reprojection error while utilizing a small multi-layer perception (MLP) to perform regression on the return energy of the sensor around the targets. The proposed approach uses a deep learning framework such as PyTorch and can be optimized through gradient descent. The experiment uses a 360-degree Ouster-128 LIDAR and a 360-degree Navtech RADAR, providing raw measurements. The results validate the effectiveness of the proposed method in achieving improved estimates of extrinsic calibration parameters.