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dc.contributor.authorChang, Yun
dc.contributor.authorEbadi, Kamak
dc.contributor.authorDenniston, Christopher E
dc.contributor.authorGinting, Muhammad Fadhil
dc.contributor.authorRosinol, Antoni
dc.contributor.authorReinke, Andrzej
dc.contributor.authorPalieri, Matteo
dc.contributor.authorShi, Jingnan
dc.contributor.authorChatterjee, Arghya
dc.contributor.authorMorrell, Benjamin
dc.contributor.authorAgha-mohammadi, Ali-akbar
dc.contributor.authorCarlone, Luca
dc.date.accessioned2022-09-07T18:08:18Z
dc.date.available2022-09-07T18:08:18Z
dc.date.issued2022-10
dc.identifier.urihttps://hdl.handle.net/1721.1/145302
dc.description.abstractSearch 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.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/lra.2022.3191204en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleLAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environmentsen_US
dc.typeArticleen_US
dc.identifier.citationChang, 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.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
dc.relation.journalIEEE Robotics and Automation Lettersen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-09-07T18:03:52Z
dspace.orderedauthorsChang, Y; Ebadi, K; Denniston, CE; Ginting, MF; Rosinol, A; Reinke, A; Palieri, M; Shi, J; Chatterjee, A; Morrell, B; Agha-mohammadi, A-A; Carlone, Len_US
dspace.date.submission2022-09-07T18:03:54Z
mit.journal.volume7en_US
mit.journal.issue4en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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