Virtual content instability caused by device pose tracking error remains a
prevalent issue in markerless augmented reality (AR), especially on smartphones
and tablets. However, when examining environments which will host AR
experiences, it is challenging to determine where those instability artifacts
will occur; we rarely have access to ground truth pose to measure pose error,
and even if pose error is available, traditional visualizations do not connect
that data with the real environment, limiting their usefulness. To address
these issues we present SiTAR (Situated Trajectory Analysis for Augmented
Reality), the first situated trajectory analysis system for AR that
incorporates estimates of pose tracking error. We start by developing the first
uncertainty-based pose error estimation method for visual-inertial simultaneous
localization and mapping (VI-SLAM), which allows us to obtain pose error
estimates without ground truth; we achieve an average accuracy of up to 96.1%
and an average F1 score of up to 0.77 in our evaluations on four VI-SLAM
datasets. Next we present our SiTAR system, implemented for ARCore devices,
combining a backend that supplies uncertainty-based pose error estimates with a
frontend that generates situated trajectory visualizations. Finally, we
evaluate the efficacy of SiTAR in realistic conditions by testing three
visualization techniques in an in-the-wild study with 15 users and 13 diverse
environments; this study reveals the impact both environment scale and the
properties of surfaces present can have on user experience and task
performance.Comment: To appear in Proceedings of IEEE ISMAR 202