SLAM-share: visual simultaneous localization and mapping for real-time multi-user augmented reality
SLAM-share: visual simultaneous localization and mapping for real-time multi-user augmented reality
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DOI：
10.1145/3555050.3569142
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发表时间：
2022-11
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影响因子：
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通讯作者：
Aditya Dhakal;Xukan Ran;Yunshu Wang;Jiasi Chen;K. Ramakrishnan;adhak
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作者：
Aditya Dhakal;Xukan Ran;Yunshu Wang;Jiasi Chen;K. Ramakrishnan;adhak
增强现实（AR）设备执行视觉同步定位和映射（SLAM）来映射现实世界并在其中定位自己，使它们能够适当地渲染虚拟全息图。目前的多用户AR平台的不足之处在于，它们只允许不对称地共享这些SLAM信息，导致多个“辅助”设备查看单个“主”设备放置的全息图，而不是平等参与。这项工作的目标是通过构建一个所有AR设备都可以贡献的通用全球地图，使所有AR设备都能平等参与。然而，在资源受限的移动设备上，以低延迟和高精度来实现这一目标是一项挑战。这项工作提出了客户端和服务器之间的适当分区，以实现高吞吐量，低延迟，多用户SLAM。在我们的SLAM- share系统中，边缘服务器执行复杂的SLAM计算，因此客户端设备只需要执行轻量级操作。服务器利用共享内存和高效的地图合并来构建和更新来自不同客户端的全局地图。它还利用GPU处理的并行性来实现高性能跟踪。评估表明，SLAM-Share能够实现显著的跟踪速度提升（与其他方法相比，最多减少50%），保持良好的定位精度，并在200毫秒内合并和更新地图。
Augmented reality (AR) devices perform visual simultaneous localization and mapping (SLAM) to map the real world and localize themselves in it, enabling them to render the virtual holograms appropriately. Current multi-user AR platforms fall short in that they only allow asymmetric sharing of this SLAM information, resulting in multiple "secondary" devices viewing holograms placed by a single "primary" device, instead of equal participation. The goal of this work is to enable all AR devices to participate equally, by constructing a common global map to which all AR devices can contribute. However, doing so with low latency and high accuracy is challenging on resource-constrained mobile devices. This work proposes an appropriate partitioning between clients and a server to achieve high-throughput, low latency, multi-user SLAM. In our system, SLAM-Share, the edge server performs the complex SLAM computations so that the client devices need only perform lightweight operations. The server utilizes shared memory and efficient map merging to build and update a global map from different clients. It also exploits the parallelism of GPU processing to achieve high-performance tracking. Evaluations show that SLAM-Share is able to achieve significant tracking speedups (up to 50% reduction compared to alternative approaches), maintain good localization accuracy, and merge and update maps within 200 ms.