SwarmMap: Scaling Up Real-time Collaborative Visual SLAM at the Edge

Authors: 

Jingao Xu, Hao Cao, and Zheng Yang, Tsinghua University; Longfei Shangguan, University of Pittsburgh & Microsoft; Jialin Zhang, Xiaowu He, and Yunhao Liu, Tsinghua University

Abstract: 

The Edge-based Multi-agent visual SLAM plays a key role in emerging mobile applications such as search-and-rescue, inventory automation, and drone grouping. This algorithm relies on a central node to maintain the global map and schedule agents to execute their individual tasks. However, as the number of agents continues growing, the operational overhead of the visual SLAM system such as data redundancy, bandwidth consumption, and localization errors also scale, which challenges the system scalability.

In this paper, we present the design and implementation of SwarmMap, a framework design that scales up collaborative visual SLAM service in edge offloading settings. At the core of SwarmMap are three simple yet effective system modules — a change log-based server-client synchronization mechanism, a priority-aware task scheduler, and a lean representation of the global map that work hand-in-hand to address the data explosion caused by the growing number of agents. We make SwarmMap compatible with the robotic operating system (ROS) and open-source it. Existing visual SLAM applications could incorporate SwarmMap to enhance their performance and capacity in multi-agent scenarios. Comprehensive evaluations and a three-month case study at one of the world's largest oil fields demonstrate that SwarmMap can serve 2× more agents (>20 agents) than the state of the arts with the same resource overhead, meanwhile maintaining an average trajectory error of 38cm, outperforming existing works by > 55%.

NSDI '22 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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BibTeX
@inproceedings {278320,
author = {Jingao Xu and Hao Cao and Zheng Yang and Longfei Shangguan and Jialin Zhang and Xiaowu He and Yunhao Liu},
title = {{SwarmMap}: Scaling Up Real-time Collaborative Visual {SLAM} at the Edge},
booktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
year = {2022},
isbn = {978-1-939133-27-4},
address = {Renton, WA},
pages = {977--993},
url = {https://www.usenix.org/conference/nsdi22/presentation/xu},
publisher = {USENIX Association},
month = apr,
}

Presentation Video