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Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0003-1189-6634
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. (RPL/EECS)ORCID iD: 0000-0002-7796-1438
2018 (English)In: 2018 IEEE OES Autonomous Underwater Vehicle Symposium, 2018Conference paper, Published paper (Refereed)
Abstract [en]

With dead-reckoning from velocity sensors,AUVs may construct short-term, local bathymetry mapsof the sea floor using multibeam sensors. However, theposition estimate from dead-reckoning will include somedrift that grows with time. In this work, we focus on long-term onboard storage of these local bathymetry maps,and the alignment of maps with respect to each other. Wepropose using Sparse Gaussian Processes for this purpose,and show that the representation has several advantages,including an intuitive alignment optimization, data com-pression, and sensor noise filtering. We demonstrate thesethree key capabilities on two real-world datasets.

Place, publisher, year, edition, pages
2018.
Keywords [en]
AUV, SLAM, Bathymetric Mapping
National Category
Robotics
Research subject
Vehicle and Maritime Engineering; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-250895OAI: oai:DiVA.org:kth-250895DiVA, id: diva2:1314054
Conference
2018 IEEE OES Autonomous Underwater Vehicle Symposium
Projects
SMaRC, SSF IRC15-0046
Funder
Swedish Foundation for Strategic Research , IRC15-0046
Note

QC 20190408

Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-05-07Bibliographically approved

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fulltext(1211 kB)54 downloads
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CiteExportLink to record
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