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

2026-06-24

AI Summary

This work addresses the challenge of extrinsic calibration for non-overlapping multi-camera systems by proposing a novel method that requires only pure rotational motion and a single static calibration target. By introducing an implicit turntable coordinate frame and formulating a 3D reprojection error on the SE(3) manifold, the approach integrates observations of the same calibration board captured by different cameras at distinct time instances into a unified global nonlinear optimization framework. The method eliminates the need for large calibration patterns or complex motion estimation, thereby avoiding scale ambiguity and drift issues. High accuracy, strong robustness, and ease of deployment are demonstrated on both controlled rigs and real-world vehicle platforms, marking the first successful realization of high-precision extrinsic calibration for non-overlapping multi-camera setups using only pure rotation and a single static target.

Abstract

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.

Problem

• extrinsic calibration
• non-overlapping FOVs
• multi-camera systems
• pure rotation
• calibration

Innovation

• extrinsic calibration
• non-overlapping FOV
• pure rotation
• latent turntable frame
• global optimization