From Uncertainty to Calibration: Online Pose Estimation of an Industrial Twin Robotic Computed Tomography System with Unknown Spheres

Abstract

Robotic CT systems offer several advantages over conventional systems due to their high flexibility. They can perform almost any CT trajectory and are particularly well suited for region-of-interest (ROI) scans of objects that exceed the size limitations of conventional CT systems. However, robot-based manipulators have a significantly lower absolute positioning accuracy compared to conventional manipulators, necessitating additional calibration methods to refine the geometric information about the spatial position and orientation of the X-ray source and detector for each projection for higher resolution reconstructions. We propose a geometric calibration method for CT systems with twelve degrees of freedom that does not require additional calibration scans. The method is easy to use, computationally efficient, and supports continuous CT trajectories. It utilises spheres with unknown positions that are attached to the specimen. The calibration process is divided into two stages. First, the spatial positions of the spheres are estimated using the initial geometric information and the acquired projections. Second, these estimates serve as input to an iterative optimisation that calibrates each projection individually. The applicability of the proposed method is demonstrated through simulations and real-world scans using a twin robotic CT system. Both quantitative and qualitative evaluations show a significant improvement in scan quality, comparable to results obtained via offline calibration. Moreover, evaluations on simulated data confirm the method’s robustness even for systems with positioning errors in the millimetre range. This novel online calibration technique is computationally efficient, compatible with highly flexible CT systems, and holds promise for enabling future mobile CT applications.