6D Object Pose Tracking for Orthopedic Surgical Training using Visual-Inertial Sensor Fusion

Digital training simulators play a growing role in orthopedic surgery, offering realistic, standardized, and risk-free learning environments without the need for constant expert supervision. To enable simulators with realistic tactile feedback and haptic sensations, accurate tracking of surgical tools and anatomical structures in real-time is required. However, existing object tracking solutions are often expensive, difficult to integrate into training workflows, or lack robustness. To address these limitations, we propose a novel visual-inertial 6D object pose tracking system for orthopedic surgical training. Our approach features a custom fiducial object that combines multiple ArUco markers with an Inertial Measurement Unit, a dual-camera setup to improve occlusion robustness, and a sensor fusion algorithm that integrates high-frequency IMU data with vision-based tracking while ensuring precise coordinate and time synchronization. In our evaluation, we achieve a fiducial object pose accuracy of 0.9 mm/0.5° and extract drill hole metrics in a mock surgical procedure with average position, angle, and length errors of 1.7 mm, 2.0°, and 1.0 mm, respectively, while demonstrating low occlusion rates. Our cost-effective and easily integrated solution meets clinical training requirements and marks a step towards scalable and widely accessible digital orthopedic simulators. The tracking code is available at https://github.com/MountainCoot/fusionpose.