针对超大型导管架井口同心度检测中空心圆柱体精确拟合难题，尤其是特征点数量少、分布不均、几何强度弱等条件下易出现初值偏差大、迭代难收敛的问题，提出一种融合随机抽样一致性（RANSAC）、主成分分析（PCA）、列文伯格-马夸尔特（LM）算法的鲁棒拟合方法：首先联合RANSAC与PCA快速估计圆柱轴线初值；继而基于距离直方图双峰检测自动判别内外壁点，免除人工标记；最后采用自适应阻尼LM算法实现参数非线性精确拟合，实现收敛速度与计算稳定性兼具之效果。模拟与实测结果显示，本文算法的特征点均方根（RMS）、内外半径拟合精度、轴线方向误差均显著优于传统方法，验证了方法的有效性与工程实用性。

This paper addresses the challenge of accurate fitting of a hollow cylinder for concentricity detection of wellhead on a super-large jacket platform， particularly under conditions of sparse and unevenly distributed feature points and weak geometric constraints， which are often prone to significant initial deviation and poor convergence. A robust fitting method integrating random sample consensus（RANSAC）， principal component analysis（PCA）， and Levenberg-Marquardt（LM） algorithms is proposed. The method begins by combining RANSAC and PCA to rapidly estimate the initial axis of the cylinder. The inner and outer wall points are then automatically distinguished based on the bimodal distribution of the distance histogram， eliminating the need for manual labeling. Finally， an adaptive damping LM algorithm is applied to achieve precise nonlinear parameter fitting， ensuring both convergence speed and stability. Simulations and field tests show that the proposed algorithm significantly outperforms conventional methods in terms of axis orientation error， fitting accuracy of inner and outer radii， and the root mean square （RMS） of feature point residuals， demonstrating its effectiveness and practical value in engineering applications.