  Visual Inertial Odometry (VIO) is an essential component of modern Augmented
Reality (AR) applications. However, VIO only tracks the relative pose of the
device, leading to drift over time. Absolute pose estimation methods infer the
device's absolute pose, but their accuracy depends on the input quality. This
paper introduces VIO-APR, a new framework for markerless mobile AR that
combines an absolute pose regressor (APR) with a local VIO tracking system.
VIO-APR uses VIO to assess the reliability of the APR and the APR to identify
and compensate for VIO drift. This feedback loop results in more accurate
positioning and more stable AR experiences. To evaluate VIO-APR, we created a
dataset that combines camera images with ARKit's VIO system output for six
indoor and outdoor scenes of various scales. Over this dataset, VIO-APR
improves the median accuracy of popular APR by up to 36\% in position and 29\%
in orientation, increases the percentage of frames in the high ($0.25 m,
2^{\circ}$) accuracy level by up to 112\% and reduces the percentage of frames
predicted below the low ($5 m, 10^\circ$) accuracy greatly. We implement
VIO-APR into a mobile AR application using Unity to demonstrate its
capabilities. VIO-APR results in noticeably more accurate localization and a
more stable overall experience.
