Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping
Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping
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DOI：
10.1145/3561972
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发表时间：
2020-06
影响因子：
2
通讯作者：
Ali J. Ben Ali-Ali-J.-Ben-Ali-46653644;Z. S. Hashemifar;Karthik Dantu
中科院分区：
文献类型：
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作者：
Ali J. Ben Ali-Ali-J.-Ben-Ali-46653644;Z. S. Hashemifar;Karthik Dantu
城市环境中的本地化变得越来越重要，并用于Arcore [18]，Arkit [34]等工具中。实现准确的室内定位和空间图的一种流行机制是使用视觉同时定位和映射（Visual-Slam）。但是，众所周知，Visual-Slam在记忆和处理时间上是资源密集的。此外，某些操作随着时间的流逝而变得复杂，这使得连续运行在移动设备上的挑战。 Edge Computing向移动设备提供了其他计算和内存资源，以允许卸载任务，而无需在将云到云时看到的较大延迟。在本文中，我们介绍了Edge-Slam，该系统使用边缘计算资源来卸载Visual-Slam的一部分。我们将Orb-Slam2 [50]用作典型的视觉 - Slam系统，并将其修改为边缘和移动设备之间的拆分体系结构。我们将跟踪计算保留在移动设备上，并将其余的计算（即本地映射和循环关闭）移至边缘。我们在这项工作中描述了设计选择，并在我们的原型中实现它们。我们的结果表明，我们的分裂体系结构可以长期使用有限的资源来运行Visual-Slam系统，而不会影响操作的准确性。它还可以使移动设备恒定的计算和内存成本保持不变，这将允许部署使用Visual-Slam的其他最终应用程序。我们执行详细的性能和资源使用（CPU，内存，网络和功率分析），以充分了解我们提出的拆分体系结构的效果。
Localization in urban environments is becoming increasingly important and used in tools such as ARCore [18], ARKit [34] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Furthermore, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading tasks without the large latencies seen when offloading to the cloud. In this article, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 [50] as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closing, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant, which would allow for the deployment of other end applications that use Visual-SLAM. We perform a detailed performance and resources use (CPU, memory, network, and power) analysis to fully understand the effect of our proposed split architecture.