From Swept Contact to Pose: Probe-Aware Registration Via Complementary-Shape Docking
Chen Chen, Yunwen Li, Yifan Xu, Xiangjie Yan, Chang Shu, Jianxia Hou, Shiji Song, Xiang LI
AI summary
Problem
Optical registration methods suffer from long calibration chains, line-of-sight constraints, and fabrication errors, while existing contact-based approaches oversimplify probe geometry and rely on fragile point correspondences. The paper addresses the need for a calibration-free registration method that explicitly models true probe geometry and leverages both contact and non-contact trajectory evidence.
Approach
The method treats registration as a docking problem between the target object and the probe’s full swept volume, using a two-stage global search with FFT-based translation correlation and low-discrepancy orientation sampling, followed by continuous SE(3) refinement via Lie-algebra updates and analytic contact sensitivities.
Key results
- Sub-0.04 mm and sub-0.4° accuracy in free-form mesh simulations
- 0.42 mm and 3.75° registration error on a tooth-preparation robot
- Outperforms optical tracker pipelines without requiring external sensors
- Robust performance under pose noise and intermittent contact loss
Why it matters
Provides a practical, high-precision registration strategy for surgical and industrial robots by eliminating reliance on fragile optical tracking and external hardware.
Abstract
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.