| dc.creator |
Chang, Yun |
|
| dc.creator |
Ebadi, Kamak |
|
| dc.creator |
Denniston, Christopher E |
|
| dc.creator |
Ginting, Muhammad Fadhil |
|
| dc.creator |
Rosinol, Antoni |
|
| dc.creator |
Reinke, Andrzej |
|
| dc.creator |
Palieri, Matteo |
|
| dc.creator |
Shi, Jingnan |
|
| dc.creator |
Chatterjee, Arghya |
|
| dc.creator |
Morrell, Benjamin |
|
| dc.creator |
Agha-mohammadi, Ali-akbar |
|
| dc.creator |
Carlone, Luca |
|
| dc.date |
2022-09-07T18:08:18Z |
|
| dc.date |
2022-09-07T18:08:18Z |
|
| dc.date |
2022-10 |
|
| dc.date |
2022-09-07T18:03:52Z |
|
| dc.date.accessioned |
2023-02-17T20:09:49Z |
|
| dc.date.available |
2023-02-17T20:09:49Z |
|
| dc.identifier |
https://hdl.handle.net/1721.1/145302 |
|
| dc.identifier |
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). |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/242161 |
|
| dc.description |
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. |
|
| dc.format |
application/pdf |
|
| dc.language |
en |
|
| dc.publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
|
| dc.relation |
10.1109/lra.2022.3191204 |
|
| dc.relation |
IEEE Robotics and Automation Letters |
|
| dc.rights |
Creative Commons Attribution-Noncommercial-Share Alike |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
|
| dc.source |
arXiv |
|
| dc.title |
LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments |
|
| dc.type |
Article |
|
| dc.type |
http://purl.org/eprint/type/JournalArticle |
|