High-Precision Pose Estimation of Medical Targets Using a Distortion Compensation Model for Robotic Surgical Navigation *

Abstract

Medical tracking is a significant issue in vision-based robotic-assisted surgical navigation, especially for distal locking of intramedullary nails. Existing solutions face limitations such as high manufacturing costs for targets, complex tracking schemes, and low positioning<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> precision. This paper proposes a novel method to estimate the pose of medical targets through pre-calibration and the Perspective-n-Point (PnP) algorithm, which determines the position of the distal intramedullary nail hole and projects this position onto the monitor. The precision of medical target positioning is highly affected by the distortion coefficient of camera internal parameters. To address this, we design and construct a distortion compensation model to reduce its impact on positioning precision. Additionally, to mitigate the effect of illumination variations, automatic exposure of polarized vision is utilized. Through 50 reprojection experiments, the proposed distortion model achieves a positioning precision of 0.284 mm at a working distance of one meter, significantly outperforming the 0.4 mm precision of the division model and the 0.426 mm precision of the polynomial model with a relative improvement of 30% and 34.2%. This method enhances the accuracy and reliability of robot-assisted surgical navigation, facilitating more precise and efficient surgical procedures.