# Calousel: Extrinsic Calibration of Non-overlapping Multi-camera Systems from Pure Rotation

arXiv: https://arxiv.org/abs/2606.25646 · HTML: https://mlsanity.com/paper/2606.25646/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.06650/) (2023)
- [Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation](https://mlsanity.com/paper/2306.12996/) (2023)
- [Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods](https://mlsanity.com/paper/1907.12425/) (2019)
- [Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information](https://mlsanity.com/paper/1902.10586/) (2019)
- [Unbiased Estimator for Distorted Conics in Camera Calibration](https://mlsanity.com/paper/2403.04583/) (2024)

## Abstract (arXiv)

Extrinsic calibration of multi-camera systems with non-overlapping FOVs has been a challenging problem in the robotics literature. Conventional target-based methods impose substantial target setup overhead, either deploying large calibration targets or requiring pre-measured multi-target poses. Motion-based approaches instead suffer from drift error, scale ambiguity, and motion degeneracy. Securing both accuracy and usability, we propose a novel calibration method that leverages pure rotational motion, requiring only a single static calibration board. The key idea is to make all cameras sequentially observe the same target under a shared geometric reference, even without overlapping views. To integrate these time-separated observations, we formulate the problem using a latent turntable frame and a 3D error on SE(3) within a global optimization framework. We validate the proposed method on both a controlled camera rig and a full-scale vehicle platform with heterogeneous cameras, and analyze robustness under non-ideal turntable motion. Extensive experiments show that our approach maintains competitive accuracy without specialized precision hardware, proving its strong suitability for realistic on-site deployments. Our code is publicly available here.