  Edge-device collaboration has the potential to facilitate compute-intensive
device pose tracking for resource-constrained mobile augmented reality (MAR)
devices. In this paper, we devise a 3D map management scheme for edge-assisted
MAR, wherein an edge server constructs and updates a 3D map of the physical
environment by using the camera frames uploaded from an MAR device, to support
local device pose tracking. Our objective is to minimize the uncertainty of
device pose tracking by periodically selecting a proper set of uploaded camera
frames and updating the 3D map. To cope with the dynamics of the uplink data
rate and the user's pose, we formulate a Bayes-adaptive Markov decision process
problem 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 data
rate, thereby supporting 3D map management. Second, utilizing extensive
generated data provided by the DT, a model-based reinforcement learning
algorithm is developed to manage the 3D map while adapting to these dynamics.
Numerical results demonstrate that the designed DT outperforms Markov models in
accurately capturing the time-varying uplink data rate, and our devised
DT-based 3D map management scheme surpasses benchmark schemes in reducing
device pose tracking uncertainty.
