Circular targets are widely used in LiDAR-camera extrinsic calibration due to their geometric consistency and ease of detection. However, achieving accurate 3D-2D circular center correspondence remains challenging. Existing methods often fail due to decoupled 3D fitting and erroneous 2D ellipse-center estimation. To address this, we propose a geometrically principled framework featuring two innovations: (i) a robust 3D circle center estimator based on conformal geometric algebra and RANSAC; and (ii) a chord-length variance minimization method to recover the true 2D projected center, resolving its dual-minima ambi- guity via homography validation or a quasi-RANSAC fallback. Evaluated on synthetic and real-world datasets, our framework significantly outperforms state-of-the-art approaches. It reduces extrinsic estimation error and enables robust calibration across diverse sensors and target types, including natural circular objects. Our code will be publicly released for reproducibility.

翻译：圆形标靶因其几何一致性及易于检测的特性，被广泛应用于激光雷达与相机的外参标定。然而，实现精确的三维-二维圆形中心对应关系仍具挑战性。现有方法常因三维拟合与二维椭圆中心估计的解耦及误差而失效。为此，我们提出一种基于几何原理的框架，包含两项创新：(i) 基于共形几何代数与RANSAC的鲁棒三维圆中心估计器；(ii) 通过弦长方差最小化方法恢复真实二维投影中心，并利用单应性验证或准RANSAC备用方案解决其双极小值模糊性问题。在合成与真实数据集上的评估表明，本框架显著优于现有先进方法，能够降低外参估计误差，并在多种传感器与标靶类型（包括自然圆形物体）上实现鲁棒标定。我们将公开代码以确保可复现性。