让机器人在无网络时也能协同定位，自组织分布式视觉定位系统
Self-Organizing Edge Computing Distribution Framework for Visual SLAM
- 构建三层自组织架构，可在无网络下跨设备协同运行SLAM
- 与单体ORBSLAM3相比定位精度持平，资源利用更高效
- 适合边缘计算、移动机器人导航等对稳定性要求高的场景
在已知环境中定位是移动机器人的重要能力。同时定位与地图构建（SLAM）是解决该问题的主流方案，其包含从实时跟踪到计算密集型地图优化等多种任务，对资源受限的移动机器人构成挑战。以往基于边缘辅助的SLAM方法通过将重负载任务卸载至云端实现实时性，但依赖客户端-服务器架构，易受服务器或网络故障影响。本文提出一种新型边缘辅助SLAM框架，可自组织地在多设备间分布执行或独立于单设备运行，无需网络连接。该架构分为三层，具备设备无关性、抗网络故障能力，且对核心SLAM系统侵入极小。我们在单目ORBSLAM3上实现了该框架，并在完全分布式和独立运行两种模式下进行评估。实验结果表明，新设计在定位精度和资源利用率方面与单体式方法相当，同时支持协同执行。
原文摘要 · Abstract (English)
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.
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