MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR
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arXiv
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| Format: | Preprint |
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2024
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| author | Liu, Changkun Zhao, Yukun Braud, Tristan |
| author_facet | Liu, Changkun Zhao, Yukun Braud, Tristan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR Liu, Changkun Zhao, Yukun Braud, Tristan Computer Vision and Pattern Recognition 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. |
| title | MobileARLoc: On-device Robust Absolute Localisation for Pervasive Markerless Mobile AR |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.11511 |