Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM
Name
2205.12402.pdf
Description
Accepted version
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4.96 MB
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Author(s)
Denniston, Christopher E
Chang, Yun
Reinke, Andrzej
Ebadi, Kamak
Sukhatme, Gaurav S
Carlone, Luca
Morrell, Benjamin
Agha-mohammadi, Ali-akbar
Date Issued
October 2022
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Denniston, Christopher E, Chang, Yun, Reinke, Andrzej, Ebadi, Kamak, Sukhatme, Gaurav S et al. 2022. "Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM." IEEE Robotics and Automation Letters, 7 (4).
Version
Author's final manuscript
Abstract
Multi-robot SLAM systems in GPS-denied environments require loop closures to
maintain a drift-free centralized map. With an increasing number of robots and
size of the environment, checking and computing the transformation for all the
loop closure candidates becomes computationally infeasible. In this work, we
describe a loop closure module that is able to prioritize which loop closures
to compute based on the underlying pose graph, the proximity to known beacons,
and the characteristics of the point clouds. We validate this system in the
context of the DARPA Subterranean Challenge and on numerous challenging
underground datasets and demonstrate the ability of this system to generate and
maintain a map with low error. We find that our proposed techniques are able to
select effective loop closures which results in 51% mean reduction in median
error when compared to an odometric solution and 75% mean reduction in median
error when compared to a baseline version of this system with no
prioritization. We also find our proposed system is able to find a lower error
in the mission time of one hour when compared to a system that processes every
possible loop closure in four and a half hours. The code and dataset for this
work can be found https://github.com/NeBula-Autonomy/LAMP
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/lra.2022.3191156
