Reliable Automatic Calibration of a Marker-Based Position Tracking System

This paper describes an accurate vision-based position tracking system which is significantly more robust and reliable over a wide range of environments than existing approaches. Based on fiducial detection for robustness, we show how a machine-learning approach allows the development of significantly more reliable fiducial detection than has previously been demonstrated. We calibrate fiducial positions using a structure-from-motion solver. We then show how nonlinear optimization of the camera position during tracking gives accuracy comparable with full bundle adjustment but at significantly reduced cost.