A hybrid pose tracking approach for handheld augmented reality

With the rapid advances in mobile computing, handheld Augmented Reality draws increasing attention. Pose tracking of handheld devices is of fundamental importance to register virtual information with the real world and is still a crucial challenge. In this paper, we present a low-cost, accurate and robust approach combining ducial tracking and inertial sensors for handheld pose tracking. Two LEDs are used as ducial markers to indicate the position of the handheld device. They are detected by an adaptive thresholding method which is robust to illumination changes, and then tracked by a Kalman lter. By combining inclination information provided by the on-device accelerometer, 6 degree-of-freedom (DoF) pose is estimated. Handheld devices are freed from computer vision processing, leaving most computing power available for applications. When one LED is occluded, the system is still able to recover the 6-DoF pose. Performance evaluation of the proposed tracking approach is carried out by comparing with the ground truth data generated by the state-of-the-art commercial motion tracking system OptiTrack. Experimental results show that the proposed system has achieved an accuracy of 1.77 cm in position estimation and 4.15 degrees in orientation estimation.With the rapid advances in mobile computing, handheld Augmented Reality draws increasing attention. Pose tracking of handheld devices is of fundamental importance to register virtual information with the real world and is still a crucial challenge. In this paper, we present a low-cost, accurate and robust approach combining ducial tracking and inertial sensors for handheld pose tracking. Two LEDs are used as ducial markers to indicate the position of the handheld device. They are detected by an adaptive thresholding method which is robust to illumination changes, and then tracked by a Kalman lter. By combining inclination information provided by the on-device accelerometer, 6 degree-of-freedom (DoF) pose is estimated. Handheld devices are freed from computer vision processing, leaving most computing power available for applications. When one LED is occluded, the system is still able to recover the 6-DoF pose. Performance evaluation of the proposed tracking approach is carried out by comparing with the ground truth data generated by the state-of-the-art commercial motion tracking system OptiTrack. Experimental results show that the proposed system has achieved an accuracy of 1.77 cm in position estimation and 4.15 degrees in orientation estimation.C