MonoTracker: Monocular-Based Fully Automatic Registration and Real-Time Tracking Method for Neurosurgical Robots

Robot-assisted surgery has become an indispensable component in modern neurosurgical procedures. However, existing registration methods for neurosurgical robots often rely on high-end hardware and involve prolonged or unstable registration times, limiting their applicability in dynamic and time-sensitive intraoperative settings. This paper proposes a novel fully automatic monocular-based registration and real-time tracking method. First, dedicated fiducials are designed, and an automatic preoperative and intraoperative detection method for these fiducials is introduced. Second, a geometric representation of the fiducials is constructed based on a 2D KD-Tree. Through a two-stage optimization process, the depth of 2D fiducials is estimated, and 2D-3D correspondences are established to achieve monocular registration. This approach enables fully automatic intraoperative registration using only a single optical camera. Finally, a six-degree-of-freedom visual servo control strategy inspired by the mass-spring-damper system is proposed. By integrating artificial potential field and admittance control, the strategy ensures real-time responsiveness and stable tracking. Experimental results demonstrate that the proposed method achieves a registration time of 0.23 s per instance with an average error of 0.58 mm. Additionally, the motion performance of the control strategy has been validated. Preliminary experiments verify the effectiveness of MonoTracker in dynamic tracking scenarios. This method holds promise for enhancing the adaptability of neurosurgical robots and offers significant clinical application potential.