仅用一个静止标定板，通过纯旋转实现无重叠视角摄像头的精准标定。
Calousel: Extrinsic Calibration of Non-overlapping Multi-camera Systems from Pure Rotation
- 利用纯旋转运动，让各相机依次观测同一静态标定板。
- 在全局优化中引入隐式转台坐标系，实现非重叠视角下的高精度标定。
- 无需精密设备，适合真实场景快速部署，已开源代码。
无重叠视场的多摄像头系统外参标定是机器人领域的难题。传统基于目标的方法需布置大型标定物或预知多目标位姿，成本高；运动法易受漂移、尺度模糊和运动退化影响。本文提出一种新方法，仅需一个静态标定板，借助纯旋转运动，使所有相机在共享几何参考下依次观测同一目标。通过构建隐式转台坐标系，并在SE(3)空间中定义全局优化的三维误差，融合时间分离的观测数据。在受控相机阵列和全尺寸车辆平台（含异构摄像头）上验证，即使转台运动不理想也具鲁棒性。大量实验表明，该方法无需专用高精度硬件即可保持竞争力精度，适用于实际现场部署。代码已公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。