A Method for Predicting Marker Tracking Error

Many augmented reality (AR) applications use marker-based vision tracking systems to recover camera pose by detecting one or more planar landmarks. However, most of these systems do not interactively quantify the accuracy of the pose they calculate. Instead, the accuracy of these systems is either ignored, assumed to be a fixed value, or determined using error tables (constructed in an off-line ground-truthed process) along with a run-time interpolation scheme. The validity of these approaches are questionable as errors are strongly dependent on the intrinsic and extrinsic camera parameters and scene geometry. In this paper we present an algorithm for predicting the statistics of marker tracker error in real-time. Based on the scaled spherical simplex unscented transform (SSSUT), the algorithm is applied to the augmented reality toolkit plus (ARToolKitPlus). The results are validated using precision off-line photogrammetric techniques.