# From Chaos to Calibration: A Geometric Mutual Information Approach to Target-Free Camera LiDAR Extrinsic Calibration

arXiv: https://arxiv.org/abs/2311.01905 · HTML: https://mlsanity.com/paper/2311.01905/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Feature-metric Loss for Self-supervised Learning of Depth and Egomotion](https://mlsanity.com/paper/2007.10603/) (2020)
- [Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review](https://mlsanity.com/paper/2004.05224/) (2020)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry](https://mlsanity.com/paper/1804.05178/) (2018)
- [CalibDNN: Multimodal Sensor Calibration for Perception Using Deep Neural Networks](https://mlsanity.com/paper/2103.14793/) (2021)
- [Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning](https://mlsanity.com/paper/2211.03932/) (2022)
- [Automatic Multi-Sensor Extrinsic Calibration for Mobile Robots](https://mlsanity.com/paper/1906.04670/) (2019)

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

- [Environment-Driven Online LiDAR-Camera Extrinsic Calibration](https://mlsanity.com/paper/2502.00801/) (2025)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network](https://mlsanity.com/paper/2601.01188/) (2026)
- [Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching](https://mlsanity.com/paper/2506.22784/) (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)
- [Joint Multi-Camera LiDAR Extrinsic Calibration via Learned Pairwise Initialization and Geometric Refinement](https://mlsanity.com/paper/2605.31576/) (2026)

## Abstract (arXiv)

Sensor fusion is vital for the safe and robust operation of autonomous vehicles. Accurate extrinsic sensor to sensor calibration is necessary to accurately fuse multiple sensor's data in a common spatial reference frame. In this paper, we propose a target free extrinsic calibration algorithm that requires no ground truth training data, artificially constrained motion trajectories, hand engineered features or offline optimization and that is accurate, precise and extremely robust to initialization error. Most current research on online camera-LiDAR extrinsic calibration requires ground truth training data which is impossible to capture at scale. We revisit analytical mutual information based methods first proposed in 2012 and demonstrate that geometric features provide a robust information metric for camera-LiDAR extrinsic calibration. We demonstrate our proposed improvement using the KITTI and KITTI-360 fisheye data set.