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-设备协同有望促进资源受限的移动增强现实设备执行计算密集型位姿追踪任务。本文提出一种面向边缘辅助移动增强现实的三维地图管理方案：边缘服务器利用移动增强现实设备上传的相机帧，构建并更新物理环境的三维地图，以支持设备端位姿追踪。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，最小化设备位姿追踪的不确定性。为应对上行链路速率与用户位姿的动态性，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的求解方法。首先，设计数字孪生作为数据模型以捕获时变上行链路速率，进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，在适应动态环境的同时管理三维地图。数值结果表明，所设计的数字孪生在准确捕获时变上行链路速率方面优于马尔可夫模型，且基于数字孪生的三维地图管理方案在降低设备位姿追踪不确定性上优于基准方案。