Augmented Reality (AR) offers significant potential to enhance surgical procedures by overlaying critical digital information directly onto the surgeon's view, improving spatial awareness and precision. Accurate real-time tracking of surgical instruments is fundamental to the success of AR navigation systems. However, implementing established tracking modalities, such as infrared (IR) tracking using retro-reflective markers, on newer Head-Mounted Displays (HMDs) like the Magic Leap 2 presents unique challenges due to its specific sensor configuration, notably the lack of accessible IR intensity data from its Time-of-Flight (ToF) sensor and the presence of IR-cut filters on its World Cameras.

This thesis addresses these challenges through the development, implementation, and evaluation of ISITLeap, a novel system designed specifically for IR instrument tracking on the Magic Leap 2 platform. The system employs a stereo vision approach, utilizing the Magic Leap 2's built-in World Cameras configured with short exposure times, synergistically combined with a custom-designed external 760 nm IR illumination module to detect standard passive retro-reflective markers. A comprehensive software pipeline was developed, incorporating image undistortion adapted for the Magic Leap 2's equidistant projection model, marker detection involving binarization, connected components analysis, and area filtering, 3D marker reconstruction using stereo triangulation with empirically derived coordinate system corrections to reconcile Magic Leap 2 SDK data with geometric requirements, and finally, 6-DoF tool pose estimation via point-based registration. Crucially, the entire software pipeline, from image acquisition to pose estimation, operates directly on the Magic Leap 2 headset, requiring no external computing hardware.

Experimental validation successfully demonstrated the feasibility of the ISITLeap system for real-time tracking. Static evaluations characterized the system's performance, identifying an optimal working range of approximately 25-50 cm. Dynamic tracking experiments, evaluated against an NDI optical tracker using an ArUco marker as a common reference frame, yielded an overall Mean Absolute Error (MAE) of approximately 7 mm. Crucially, error analysis revealed that the measured accuracy was significantly influenced by limitations inherent in the ArUco marker-based evaluation methodology, likely masking the intrinsic performance capabilities of the ISITLeap core tracking system.

This work presents a complete, functional hardware and software solution tailored to overcome the specific sensor constraints of the Magic Leap 2, enabling IR marker tracking on this platform. It provides a crucial performance benchmark under the defined evaluation conditions and demonstrates a viable technical pathway for developing precise AR surgical navigation applications for the Magic Leap 2 headset. While the achieved referenced accuracy highlights the need for improved evaluation techniques and further system refinement (e.g., via dynamic filtering and optimized hardware), ISITLeap establishes a foundational system for future advancements in Magic Leap 2-based AR surgical guidance.