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Visual Inertial Odometry (VIO) is an essential component of modern AugmentedReality (AR) applications. However, VIO only tracks the relative pose of thedevice, leading to drift over time. Absolute pose estimation methods infer thedevice's absolute pose, but their accuracy depends on the input quality. Thispaper introduces VIO-APR, a new framework for markerless mobile AR thatcombines 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 identifyand compensate for VIO drift. This feedback loop results in more accuratepositioning and more stable AR experiences. To evaluate VIO-APR, we created adataset that combines camera images with ARKit's VIO system output for sixindoor and outdoor scenes of various scales. Over this dataset, VIO-APRimproves 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 framespredicted below the low ($5 m, 10^\circ$) accuracy greatly. We implementVIO-APR into a mobile AR application using Unity to demonstrate itscapabilities. VIO-APR results in noticeably more accurate localization and amore stable overall experience.

Robust Localization with Visual-Inertial Odometry Constraints for Markerless Mobile AR