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Sparse Gaussian Process SLAM, Storage and Filtering for AUV Multibeam Bathymetry
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.
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]

With dead-reckoning from velocity sensors, AUVs may construct short-term, local bathymetry maps of the sea floor using multibeam sensors. However, the position estimate from dead-reckoning will include some drift 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. We propose using Sparse Gaussian Processes for this purpose, and show that the representation has several advantages, including an intuitive alignment optimization, data compression, and sensor noise filtering. We demonstrate these three key capabilities on two real-world datasets.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
Autonomous vehicles, Bathymetry, Digital storage, Gaussian distribution, Gaussian noise (electronic), Navigation, Alignment optimization, Dead reckoning, Multi-beam sensors, Multibeam bathymetry, Position estimates, Real-world datasets, Sparse Gaussian process, Velocity sensor, Autonomous underwater vehicles
National Category
Vehicle Engineering Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-262399DOI: 10.1109/AUV.2018.8729748ISI: 000492901600046Scopus ID: 2-s2.0-85068312637ISBN: 9781728102535 (print)OAI: oai:DiVA.org:kth-262399DiVA, id: diva2:1365403
Conference
2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV 2018, 6-9 November 2018, Porto, Portugal
Note

QC 20191024

Available from: 2019-10-24 Created: 2019-10-24 Last updated: 2020-01-08Bibliographically approved

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Bore, NilsTorroba, IgnacioFolkesson, John

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