Entry Point Localisation for Percutaneous Surgical Robots Based on Concentric Fiducial Patches and Robust Surface Fitting.

BACKGROUND

Accurate entry point localisation is critical for the safety of robot-assisted percutaneous interventions (RAPI). However, medical optical tracking systems for the traditional fiducial patch are cost-prohibitive. To address these challenges, this paper proposes a robust entry point and gesture localisation framework based on an inexpensive concentric fiducial patch and robust surface fitting.

METHODS

We introduce an anisotropic iteratively reweighted least squares (IRLS) algorithm integrated with a Tukey biweight M-estimator, employing a deterministic soft-weighting strategy to effectively suppress gross outliers and smooth out high-frequency sensor noise.

RESULTS

Experimental results demonstrate that the proposed method has high accuracy and robustness, achieving an MAE of 1.47 mm in dynamic respiratory tracking and maintains stable detection even under 60% occlusion and low-light conditions.

CONCLUSIONS

The results suggest that this framework provides a reliable and low-cost solution for surgical navigation in unstructured clinical environments.