In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper set of camera frames for uploading to update the 3D map. To address the challenges of the dynamic uplink data rate and the time-varying pose of the MAR device, we propose a digital twin (DT)-based approach to 3D map management. First, a DT is created for the MAR device, which emulates 3D map management based on predicting subsequent camera frames. Second, a model-based reinforcement learning (MBRL) algorithm is developed, utilizing the data collected from both the actual and the emulated data to manage the 3D map. With extensive emulated data provided by the DT, the MBRL algorithm can quickly provide an adaptive map management policy in a highly dynamic environment. Simulation results demonstrate that the proposed DT-based 3D map management outperforms benchmark schemes by achieving lower pose estimation uncertainty and higher data efficiency in dynamic environments.

翻译：本文针对边缘辅助移动增强现实（MAR）场景设计了一种三维地图管理方案，以支持单个MAR设备的姿态估计——该设备将相机帧上传至边缘服务器。我们的目标是通过周期性选择适当的相机帧集进行上传以更新三维地图，从而最小化MAR设备的姿态估计不确定性。为应对动态上行数据速率与MAR设备时变姿态的挑战，我们提出了一种基于数字孪生（DT）的三维地图管理方法。首先，为MAR设备创建数字孪生体，通过预测后续相机帧来模拟三维地图管理过程；其次，利用实际采集与模拟数据开发基于模型的强化学习（MBRL）算法以管理三维地图。借助数字孪生提供的大量仿真数据，MBRL算法可在高度动态环境中快速生成自适应地图管理策略。仿真结果表明，所提出的基于数字孪生的三维地图管理方法在动态环境下实现了更低的姿态估计不确定性和更高的数据效率，优于基准方案。