  Recent years have seen significant improvement in absolute camera pose
estimation, paving the way for pervasive markerless Augmented Reality (AR).
However, accurate absolute pose estimation techniques are computation- and
storage-heavy, requiring computation offloading. As such, AR systems rely on
visual-inertial odometry (VIO) to track the device's relative pose between
requests to the server. However, VIO suffers from drift, requiring frequent
absolute repositioning. This paper introduces MobileARLoc, a new framework for
on-device large-scale markerless mobile AR that combines an absolute pose
regressor (APR) with a local VIO tracking system. Absolute pose regressors
(APRs) provide fast on-device pose estimation at the cost of reduced accuracy.
To address APR accuracy and reduce VIO drift, MobileARLoc creates a feedback
loop where VIO pose estimations refine the APR predictions. The VIO system
identifies reliable predictions of APR, which are then used to compensate for
the VIO drift. We comprehensively evaluate MobileARLoc through dataset
simulations. MobileARLoc halves the error compared to the underlying APR and
achieve fast (80\,ms) on-device inference speed.
