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Edge-device collaboration has the potential to facilitate compute-intensivedevice pose tracking for resource-constrained mobile augmented reality (MAR)devices. In this paper, we devise a 3D map management scheme for edge-assistedMAR, wherein an edge server constructs and updates a 3D map of the physicalenvironment by using the camera frames uploaded from an MAR device, to supportlocal device pose tracking. Our objective is to minimize the uncertainty ofdevice pose tracking by periodically selecting a proper set of uploaded cameraframes and updating the 3D map. To cope with the dynamics of the uplink datarate and the user's pose, we formulate a Bayes-adaptive Markov decision processproblem and propose a digital twin (DT)-based approach to solve the problem.First, a DT is designed as a data model to capture the time-varying uplink datarate, thereby supporting 3D map management. Second, utilizing extensivegenerated data provided by the DT, a model-based reinforcement learningalgorithm is developed to manage the 3D map while adapting to these dynamics.Numerical results demonstrate that the designed DT outperforms Markov models inaccurately capturing the time-varying uplink data rate, and our devisedDT-based 3D map management scheme surpasses benchmark schemes in reducingdevice pose tracking uncertainty.

Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR