Collaborative Robot Mapping using Spectral Graph Analysis
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global map. However, inconsistencies between onboard and server estimates can occur due to onboard odometry drift or failure. Furthermore, robots do not benefit from the collaborative map if the server provides no feedback in a computationally tractable and bandwidth-efficient manner. Motivated by this challenge, this paper proposes a novel collaborative mapping framework to enable accurate global mapping among robots and server. In particular, structural differences between robot and server graphs are exploited at different spatial scales using graph spectral analysis to generate necessary constraints for the individual robot pose graphs. The proposed approach is thoroughly analyzed and validated using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90%.
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Publication status
published
Editor
Book title
2022 International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
3662 - 3668
Publisher
IEEE
Event
39th IEEE International Conference on Robotics and Automation (ICRA 2022)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Industries; Simultaneous localization and mapping; Automation; Collaboration; Computational efficiency; Servers; Robots
Organisational unit
Notes
Funding
955356 - Improved Robotic Platform to perform Maintenance and Upgrading Roadworks: The ΗΕRΟΝ Approach (EC)