Vision-based pose computation: robust and accurate augmented reality tracking

Abstract

Vision-based tracking systems have advantages for augmented reality (AR) applications. Their registration can be very accurate, and there is no delay between the motions of real and virtual scene elements. However, vision-based tracking often suffers from limited range, intermittent errors, and dropouts. These shortcomings are due to the need to see multiple calibrated features or fiducials in each frame. To address these shortcomings, features in the scene can be dynamically calibrated and pose calculations can be made robust to noise and numerical instability. In this paper, we survey classic vision-based pose computations and present two methods that offer increased robustness and accuracy in the context of real-time AR tracking.