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A Comparison of Submap Registration Methods for Multibeam Bathymetric Mapping
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-1189-6634
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-7796-1438
2018 (English)In: AUV 2018 - 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

On-the-fly registration of overlapping multibeam images is important for path planning by AUVs performing underwater surveys. In order to meet specification on such things as survey accuracy, coverage and density, precise corrections to the AUV trajectory while underway are required. There are fast methods for aligning point clouds that have been developed for robots. We compare several state of the art methods to align point clouds of large, unstructured, sub-aquatic areas to build a global map. We first collect the multibeam point clouds into smaller submaps that are then aligned using variations of the ICP algorithm. This alignment step can be applied if the error in AUV pose is small. It would be the final step in correcting a larger error on loop closing where a place recognition and a rough alignment would precede it. In the case of a lawn mower pattern survey it would be making more continuous corrections to small errors in the overlap between parallel lines. In this work we compare different methods for registration in order to determine the most suitable one for underwater terrain mapping. To do so, we benchmark the current state of the art solutions according to an error metrics and show the results.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
Autonomous vehicles, Errors, Lawn mowers, Mapping, Motion planning, Surveys, ICP algorithms, Place recognition, Registration methods, State of the art, State-of-the-art methods, Survey accuracy, Terrain mapping, Underwater surveys, Autonomous underwater vehicles
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-262474DOI: 10.1109/AUV.2018.8729731ISI: 000492901600029Scopus ID: 2-s2.0-85068333120ISBN: 9781728102535 (print)OAI: oai:DiVA.org:kth-262474DiVA, id: diva2:1361880
Conference
2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV 2018, 6 November 2018 through 9 November 2018, Porto, Portugal
Note

QC 20191017

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-11-26Bibliographically approved

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Torroba, IgnacioBore, NilsFolkesson, John

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