Augmented reality (AR) systems are often used to superimpose virtual objects or information on a scene to improve situational awareness. Delays in the display system or inaccurate registration of objects destroy the sense of immersion a user experiences when using AR systems. AC electromagnetic trackers are ideal for these applications when combined with head orientation prediction to compensate for display system delays. Unfortunately, these trackers do not perform well in environments that contain conductive or ferrous materials due to magnetic field distortion without expensive calibration techniques. In our work we focus on both the prediction and distortion compensation aspects of this application, developing a ""small footprint"" predictive filter for display lag compensation and a simplified calibration system for AC magnetic trackers.
In the first phase of our study we presented a novel method of tracking angular head velocity from quaternion orientation using an Extended Kalman Filter in both single model (DQEKF) and multiple model (MMDQ) implementations. In the second phase of our work we have developed a new method of mapping the magnetic field generated by the tracker without high precision measurement equipment. This method uses simple fixtures with multiple sensors in a rigid geometry to collect magnetic field data in the tracking volume. We have developed a new algorithm to process the collected data and generate a map of the magnetic field distortion that can be used to compensate distorted measurement data.
Author Biography:
Henry Himberg received the B.S. degree in electrical and computer engineering from Clarkson University, Potsdam, NY, in 1982, the M.S. degree from the University of Vermont, Burlington, in 2005, and the Ph.D. degree in electrical and computer engineering from Virginia Commonwealth University, Richmond, in 2010. He is currently a Staff Scientist with Polhemus, Colchester, VT. His research interests include signal processing, position/orientation measurement, and motion prediction. Yuichi Motai received the B.Eng. degree in instrumentation engineering from Keio University, Tokyo, Japan, in 1991, the M.Eng. degree in applied systems science from Kyoto University, Kyoto, Japan, in 1993, and the Ph.D. degree in electrical and computer engineering from Purdue University, West Lafayette, in 2002. He is currently an Associate Professor of Electrical and Computer Engineering at Virginia Commonwealth University, Richmond, VA. His research interests include the broad area of sensory intelligence, particularly in medical imaging, pattern recognition, computer vision, and sensory-based robotics.
