Evaluating the impact of recovery density on augmented reality tracking

Abstract

Natural feature tracking systems for augmented reality are highly accurate, but can suffer from lost tracking. When registration is lost, the system must be able to re-localize and recover tracking. Likewise, when a camera is new to a scene, it must be able to perform the related task of localization. Localization and re-localization can only be performed at certain points or when viewing particular objects or parts of the scene with a sufficient number and quality of recognizable features to allow for tracking recovery. We explore how the density of such recovery locations/poses influences the time it takes users to resume tracking. We focus our evaluation on two generalized techniques for localization: keyframe-based and model-based. For the keyframe-based approach we assume a constant collection rate for keyframes. We find that at practical collection rates, the task of localization to a previously acquired keyframe that is shown to the user does not become more time-consuming as the interval between keyframes increases. For a localization approach using model data, we consider a grid of points around the model at which localization is guaranteed to succeed. We find that the user interface is crucial to successful localization. Localization can occur quickly if users do not need to orient themselves to marked localization points. When users are forced to mentally register themselves with a map of the scene, localization quickly becomes impractical as the distance to the next localization point increases. We contend that our results will help future designers of localization techniques to better plan for the effects of their proposed solutions.