Networked Architectures for Localization-Based Multi-User Augmented Reality
Networked Architectures for Localization-Based Multi-User Augmented Reality
复制标题
DOI：
10.1109/mcom.003.2300275
复制
发表时间：
2023-12
影响因子：
11.2
通讯作者：
Jiasi Chen;K. K. Ramakrishnan-K.;Aditya Dhakazl;Xukan Ran
中科院分区：
文献类型：
--
作者：
Jiasi Chen;K. K. Ramakrishnan-K.;Aditya Dhakazl;Xukan Ran
多用户增强现实 (AR) 旨在增强共享活动中的用户体验，无论是培训、娱乐协作还是自动驾驶和驾驶员辅助等安全应用。 AR 涉及许多需要传感和复杂计算的任务。一项关键任务是在现实世界中定位 AR 设备，以便在显示器上的正确位置渲染虚拟全息图。随着云计算向边缘靠拢，可以使用许多架构选项来帮助完成这些复杂的计算，从在终端设备上执行所有计算到将其全部移动到云端，每个架构选项对通信链路的依赖程度不同。在这项工作中，我们概述了多用户 AR 本地化的几种架构替代方案及其对许多重要使用场景的适用性。所考虑的使用场景（娱乐、自动驾驶汽车和医学）反映了一系列延迟和空间精度要求。在此背景下，我们讨论了我们最近在以边缘云为中心的解决方案（SLAM-Share）方面的工作及其对各种用例的适用性。此外，我们还评估了其对通信延迟变化的恢复能力。总的来说，本文旨在概述基于本地化的 AR 网络架构，为未来 AR 系统的设计提供信息。
Multi-user augmented reality (AR) seeks to enhance the user experience in shared activities, whether collaboration for training, entertainment, or safety applications such as autonomous driving and driver assistance. AR involves a number of tasks that require both sensing and complex computations. A key task is localizing an AR device in the real world, in order to render the virtual holograms at the correct locations on the display. With cloud computing moving closer to the edge, a number of architectural options are available to aid in these complex computations, ranging from performing all of the computation at the end device to moving it all to the cloud, each with a different level of dependency on the communication link. In this work, we outline several architectural alternatives for localization in multi-user AR and their applicability to a number of important usage scenarios. The considered usage scenarios (entertainment, autonomous vehicles, and medicine) reflect a range of latency and spatial accuracy requirements. Within this context, we discuss our recent work on an edge-cloud-centric solution – SLAM-Share – and its applicability to various use cases. In addition, we evaluate its resilience to variations in communication delay. Overall, this article seeks to provide an overview of network architectures for localization-based AR to inform the design of future AR systems.