Accurate registration between a prior model and the real scene is essential for high-precision robotic manipulation, yet optical methods suffer from long calibration chains, line-of-sight constraints, and fabrication errors. We propose a calibration-free alternative that reformulates contact registration as complementary-shape docking between the object and the probe's swept volume, explicitly accounting for probe geometry and leveraging both contact and non-contact evidence. Our solver integrates a global-to-local search via 3D FFT correlation over low-discrepancy SO(3) samples, then followed by continuous SE(3) refinement using Lie-algebra updates and analytic contact sensitivities. This pipeline yields efficient exploration and metric-grade convergence without fragile point correspondences. Simulation across free-form meshes achieved sub-0.04 mm and sub-0.4° accuracy and robustness to pose noise and contact loss. On a tooth-preparation robot, our method attained 0.42 mm and 3.75°, outperforming an optical tracker registration while requiring no external sensors. These results demonstrate a practical and precise registration strategy for surgical and industrial robots.

翻译：先验模型与真实场景之间的精确配准对于高精度机器人操作至关重要，然而光学方法存在标定链长、视线约束及制造误差等问题。我们提出一种免标定替代方案，将接触配准重新定义为物体与探针扫描体积之间的互补形状对接，显式考虑探针几何结构并利用接触与非接触证据。我们的求解器整合了全局到局部的搜索策略：先对低差异SO(3)样本进行三维FFT相关运算，随后使用李代数更新和分析接触灵敏度进行连续SE(3)精化。该流程无需脆弱的点对应关系即可实现高效探索和公制级收敛。在自由形态网格上的仿真中实现了低于0.04毫米和低于0.4°的精度，且对位姿噪声和接触缺失具有鲁棒性。在牙齿预备机器人上，我们的方法达到0.42毫米和3.75°的精度，优于光学跟踪配准器且无需外部传感器。这些结果证明了一种适用于手术和工业机器人的实用精确配准策略。