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.

翻译：边缘-设备协作有潜力为资源受限的移动增强现实（MAR）设备实现计算密集型设备姿态追踪提供支持。本文提出一种面向边缘辅助MAR的三维地图管理方案：边缘服务器利用MAR设备上传的相机帧构建并更新物理环境的三维地图，以支撑本地设备姿态追踪。我们的目标是通过周期性选择合适的上传相机帧并更新三维地图，最小化设备姿态追踪的不确定性。为应对上行链路数据速率和用户姿态的动态变化，我们构建了贝叶斯自适应马尔可夫决策过程问题，并提出基于数字孪生（DT）的解决方案。首先，设计数字孪生作为数据模型来捕捉时变的上行数据速率，从而支撑三维地图管理；其次，利用数字孪生生成的海量数据，开发基于模型的强化学习算法以动态管理三维地图。数值结果表明：所设计的数字孪生在准确捕捉时变上行数据速率方面优于马尔可夫模型，基于数字孪生的三维地图管理方案在降低设备姿态追踪不确定性方面优于基准方案。