COVINS: Visual-Inertial SLAM for Centralized Collaboration
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Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Collaborative SLAM enables a group of agents to simultaneously
co-localize and jointly map an environment, thus paving the way to
wide-ranging applications of multi-robot perception and multi-user
AR experiences by eliminating the need for external infrastructure
or pre-built maps. This article presents COVINS, a novel collab-
orative SLAM system, that enables multi-agent, scalable SLAM
in large environments and for large teams of more than 10 agents.
The paradigm here is that each agent runs visual-inertial odomety
independently onboard in order to ensure its autonomy, while shar-
ing map information with the COVINS server back-end running
on a powerful local PC or a remote cloud server. The server back-
end establishes an accurate collaborative global estimate from the
contributed data, refining the joint estimate by means of place recog-
nition, global optimization and removal of redundant data, in order
to ensure an accurate, but also efficient SLAM process. A thorough
evaluation of COVINS reveals increased accuracy of the collab-
orative SLAM estimates, as well as efficiency in both removing
redundant information and reducing the coordination overhead, and
demonstrates successful operation in a large-scale mission with 12
agents jointly performing SLAM.
Permanent link
Publication status
published
Editor
Book title
2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
Journal / series
Volume
Pages / Article No.
171 - 176
Publisher
IEEE
Event
1st International Symposium on Mixed and Augmented Reality (ISMAR 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Collaborative SLAM; Computer vision; Multi-agent systems; Augmented and mixed reality; Large-scale SLAM
Organisational unit
09559 - Chli, Margarita (ehemalig) / Chli, Margarita (former)
Notes
Conference lecture held at the poster session on October 5, 2021
Funding
157585 - Collaborative vision-based perception for teams of (aerial) robots (SNF)