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Recent years have seen significant improvement in absolute camera poseestimation, paving the way for pervasive markerless Augmented Reality (AR).However, accurate absolute pose estimation techniques are computation- andstorage-heavy, requiring computation offloading. As such, AR systems rely onvisual-inertial odometry (VIO) to track the device's relative pose betweenrequests to the server. However, VIO suffers from drift, requiring frequentabsolute repositioning. This paper introduces MobileARLoc, a new framework foron-device large-scale markerless mobile AR that combines an absolute poseregressor (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 feedbackloop where VIO pose estimations refine the APR predictions. The VIO systemidentifies reliable predictions of APR, which are then used to compensate forthe VIO drift. We comprehensively evaluate MobileARLoc through datasetsimulations. MobileARLoc halves the error compared to the underlying APR andachieve fast (80\,ms) on-device inference speed.

MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR