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.

本篇论文提出了一种数字孪生基于的 3D 地图管理方案，利用模型驱动增强学习算法对摄像头逐帧预测和上传，以解决动态环境中边缘增强现实设备姿态估计不确定性和数据不足的问题。模拟结果表明，该方案可以在动态环境中实现更低的姿态估计不确定性和更高的数据效率。