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Machine Learning for Inferring Sidescan Images from Bathymetry and AUV Pose
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Underwater navigation has been a big challenge for autonomous underwater vehicles (AUVs) for a long time. It is highly dependent on acoustic methods called SONAR. There are two kinds of sonar sensors which are commonly used, the multibeam sonar and the sidescan sonar. Both of them have some advantages and limitations. Substantial improvements can be made if a machine interpretation method can be developed for the translation between these two sonar data.The objective of this thesis project is to find an effective way to do translation from seabed bathymetry (underwater depth) data (from multibeam sonar) to sidescan sonar images. In the project, we explored the feasibility of machine learning based translation methods. Some different generative models based on the idea of generative adversarial nets were tried. This project is an experimental trial, and it still needs more improvement before production. But the result shows a strong potential for the ability of machine learning based methods to handle this kind of translation tasks.

Abstract [sv]

Navigeringen har varit en stor utmaning för autonoma undervattensfordon (AUV) under lång tid. Typiskt används akustiska metoder, så kallad SONAR. Det finns två typer av sonarsensorer, multibeam sonar och sidescan sonar. Båda har styrkor och svagheter. Genom att översätta mellan dessa två sensordata kan betydande förbättringar uppnås.Syftet med detta avhandlingsprojekt är att hitta ett effektivt sätt för att översätta data från batymetri (undervattensdjup, från multibeam sonar) till sidescan sonarbilder. I projektet undersökte vi genomförbarheten för översättningsmetoder baserad på maskininlärning. Olika generativa modeller baserade på generative adversarial nets (GANs) hade undersöktes. Detta projekt kan ses som en förstudie. Ytterligare förbättringar krävs fortfarande, men resultatet visar en stark potential för maskininlärningsmetoder att hantera denna typ av översättningsuppgifter.

Place, publisher, year, edition, pages
2019. , p. 41
Series
TRITA-EECS-EX ; 2019:421
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254947OAI: oai:DiVA.org:kth-254947DiVA, id: diva2:1336657
Subject / course
Computer Science
Educational program
Master of Science - Machine Learning
Supervisors
Examiners
Available from: 2019-07-10 Created: 2019-07-10 Last updated: 2019-07-10Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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