LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments
Name
2205.13135.pdf
Description
Accepted version
Size
6.8 MB
Format
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Checksum (MD5)
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Author(s)
Chang, Yun
Ebadi, Kamak
Denniston, Christopher E
Ginting, Muhammad Fadhil
Rosinol, Antoni
Reinke, Andrzej
Palieri, Matteo
Shi, Jingnan
Chatterjee, Arghya
Morrell, Benjamin
Date Issued
October 2022
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Chang, Yun, Ebadi, Kamak, Denniston, Christopher E, Ginting, Muhammad Fadhil, Rosinol, Antoni et al. 2022. "LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments." IEEE Robotics and Automation Letters, 7 (4).
Version
Author's final manuscript
Abstract
Search and rescue with a team of heterogeneous mobile robots in unknown and
large-scale underground environments requires high-precision localization and
mapping. This crucial requirement is faced with many challenges in complex and
perceptually-degraded subterranean environments, as the onboard perception
system is required to operate in off-nominal conditions (poor visibility due to
darkness and dust, rugged and muddy terrain, and the presence of self-similar
and ambiguous scenes). In a disaster response scenario and in the absence of
prior information about the environment, robots must rely on noisy sensor data
and perform Simultaneous Localization and Mapping (SLAM) to build a 3D map of
the environment and localize themselves and potential survivors. To that end,
this paper reports on a multi-robot SLAM system developed by team CoSTAR in the
context of the DARPA Subterranean Challenge. We extend our previous work, LAMP,
by incorporating a single-robot front-end interface that is adaptable to
different odometry sources and lidar configurations, a scalable multi-robot
front-end to support inter- and intra-robot loop closure detection for large
scale environments and multi-robot teams, and a robust back-end equipped with
an outlier-resilient pose graph optimization based on Graduated Non-Convexity.
We provide a detailed ablation study on the multi-robot front-end and back-end,
and assess the overall system performance in challenging real-world datasets
collected across mines, power plants, and caves in the United States. We also
release our multi-robot back-end datasets (and the corresponding ground truth),
which can serve as challenging benchmarks for large-scale underground SLAM.
MIT Department
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.2022.3191204
