在大型构件的机器人制孔过程中，工件形变与装夹偏差常导致其实际几何特征与理论数模不一致，致使制孔位置精度与法向精度难以满足工艺要求。针对这一问题，本文提出一种基于工件局部特征匹配的加工位姿误差预测与补偿方法。该方法基于局部节点仿射变换的构件匹配框架，融合点云分割、节点映射与迭代优化策略，实现目标加工位置精度与法向精度的联合预测补偿。面向舱段结构试验样件开展制孔精度补偿试验验证，结果表明：所提方法在预测阶段可将孔位位置偏差由3.99 mm降至0.22 mm，法向偏差由0.86°降至0.09°，并在机器人制孔试验中，孔位位置精度与法向精度分别提升86.7%和87.9%。该研究为大型构件的机器人自适应加工提供了一种有效技术途径。

In the robotic drilling process of large-scale components, workpiece deformation and clamping deviations often lead to discrepancies between actual geometric features and theoretical digital models, making it difficult for hole positioning and normal vector accuracy to meet stringent process requirements. To address this issue, this paper proposes a machining pose error prediction and compensation method based on local feature matching of the workpiece. Within a component matching framework utilizing local node affine transformation, the method integrates point cloud segmentation, node mapping, and iterative optimization strategies to achieve the joint prediction and compensation of target machining position and normal accuracy. Experimental validation was conducted on a cabin structural test specimen. The results demonstrate that during the prediction phase, the proposed method reduces the hole position deviation from 3.99 mm to 0.22 mm and the normal deviation from 0.86° to 0.09°. In actual robotic drilling tests, the positioning and normal accuracy were improved by 86.7% and 87.9%, respectively. This research provides an effective technical approach for the adaptive robotic machining of large-scale components.