# RLCNet: An end-to-end deep learning framework for simultaneous online calibration of LiDAR, RADAR, and Camera

arXiv: https://arxiv.org/abs/2512.08262 · HTML: https://mlsanity.com/paper/2512.08262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 9 Dec 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox](https://mlsanity.com/paper/2302.05094/) (2023)
- [Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups](https://mlsanity.com/paper/2101.04431/) (2021)
- [Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups](https://mlsanity.com/paper/1705.04085/) (2017)
- [Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions](https://mlsanity.com/paper/2101.11440/) (2021)
- [3D Radar and Camera Co-Calibration: A Flexible and Accurate Method for Target-based Extrinsic Calibration](https://mlsanity.com/paper/2307.15264/) (2023)
- [Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera](https://mlsanity.com/paper/2309.00787/) (2023)
- [From Chaos to Calibration: A Geometric Mutual Information Approach to Target-Free Camera LiDAR Extrinsic Calibration](https://mlsanity.com/paper/2311.01905/) (2023)
- [Spatiotemporal Calibration of 3D Millimetre-Wavelength Radar-Camera Pairs](https://mlsanity.com/paper/2211.01871/) (2022)
- [Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning](https://mlsanity.com/paper/2206.02856/) (2022)
- [An Interactive LiDAR to Camera Calibration](https://mlsanity.com/paper/1903.02122/) (2019)

## Abstract (arXiv)

Accurate extrinsic calibration of LiDAR, RADAR, and camera sensors is essential for reliable perception in autonomous vehicles. Still, it remains challenging due to factors such as mechanical vibrations and cumulative sensor drift in dynamic environments. This paper presents RLCNet, a novel end-to-end trainable deep learning framework for the simultaneous online calibration of these multimodal sensors. Validated on real-world datasets, RLCNet is designed for practical deployment and demonstrates robust performance under diverse conditions. To support real-time operation, an online calibration framework is introduced that incorporates a weighted moving average and outlier rejection, enabling dynamic adjustment of calibration parameters with reduced prediction noise and improved resilience to drift. An ablation study highlights the significance of architectural choices, while comparisons with existing methods demonstrate the superior accuracy and robustness of the proposed approach.