MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR
This addresses the need for accurate, on-device localization in pervasive markerless mobile AR, though it is incremental as it builds on existing APR and VIO methods.
The paper tackles the problem of drift in visual-inertial odometry for mobile AR by introducing MobileARLoc, a framework that combines an absolute pose regressor with VIO in a feedback loop, resulting in halved error and 80 ms on-device inference speed.
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.