面向视觉SLAM的自组织边缘计算分布式框架
Self-Organizing Edge Computing Distribution Framework for Visual SLAM
AI总结：
针对资源受限移动机器人运行SLAM的算力瓶颈及传统客户端-服务器边缘辅助方案的故障敏感问题，提出一种三层结构、设备无关、抗网络故障的自组织分布式边缘辅助SLAM框架，基于单目ORB SLAM3验证其在协同执行下精度与资源利用率与单体方案相当。
摘要：
已知环境中的定位是移动机器人的一项核心能力。同步定位与建图（Simultaneous Localization and Mapping，SLAM）是解决该问题的主流方案。SLAM是一套包含多种计算任务的框架，任务范围从实时跟踪到计算密集型的地图优化不等。这种任务组合对资源有限的移动机器人而言是一项挑战。此前，边缘辅助SLAM方法通过将繁重计算任务卸载、同时在本地执行实时跟踪，已展现出可观的实时运行能力。但这类常用的客户端-服务器架构卸载方案对服务器故障和网络故障较为敏感。本文提出一种新型边缘辅助SLAM框架，它既能在设备网络中自组织完成完全分布式的SLAM执行，也能在无网络连接时在单设备上运行。该架构包含三层，设计上具备设备无关性、网络故障弹性，且对核心SLAM系统的侵入性极低。我们基于单目ORB SLAM3实现并验证了该框架，在完全分布式和单机SLAM两种配置下与ORB SLAM3进行了对比评估。实验结果表明，所提设计在实现协同执行的同时，精度与资源利用率与单体式方案相当。
英文摘要：
Localization within a known environment is a crucial capability for mobile robots. Simultaneous Localization and Mapping (SLAM) is a prominent solution to this problem. SLAM is a framework that consists of a diverse set of computational tasks ranging from real-time tracking to computation-intensive map optimization. This combination can present a challenge for resource-limited mobile robots. Previously, edge-assisted SLAM methods have demonstrated promising real-time execution capabilities by offloading heavy computations while performing real-time tracking onboard. However, the common approach of utilizing a client-server architecture for offloading is sensitive to server and network failures. In this article, we propose a novel edge-assisted SLAM framework capable of self-organizing fully distributed SLAM execution across a network of devices or functioning on a single device without connectivity. The architecture consists of three layers and is designed to be device-agnostic, resilient to network failures, and minimally invasive to the core SLAM system. We have implemented and demonstrated the framework for monocular ORB SLAM3 and evaluated it in both fully distributed and standalone SLAM configurations against the ORB SLAM3. The experiment results demonstrate that the proposed design matches the accuracy and resource utilization of the monolithic approach while enabling collaborative execution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
