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GPS-level accurate camera localization with HorizonNet
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Saab Dynam, Dept Dev and Technol, Linkoping, Sweden.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Inception Inst Artificial Intelligence, U Arab Emirates.
2020 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967Article in journal (Refereed) Epub ahead of print
Abstract [en]

This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360 degrees panoramic image around the USV. We design a convolutional neural network (CNN) architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model data in the Fourier domain using a minimum output sum of squared error correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in field trials conducted over 3 days in the archipelago. Our approach provides excellent results by achieving robust position estimates with global positioning system (GPS)-level accuracy in previously unvisited test areas.

Place, publisher, year, edition, pages
WILEY , 2020.
Keywords [en]
GPS-denied operation; localization; marine robotics
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-163032DOI: 10.1002/rob.21929ISI: 000503992000001OAI: oai:DiVA.org:liu-163032DiVA, id: diva2:1384261
Note

Funding Agencies|Wallenberg AI, Autonomous Systems, and Software Program (WASP) - Knut and Alice Wallenberg Foundation; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RIT 15-0097]; CENIIT grant [18.14]; VR starting grant [2016-05543]

Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-03-11

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The full text will be freely available from 2020-12-23 12:56
Available from 2020-12-23 12:56

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