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HorizonNet for visual terrain navigation
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.
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.
2018 (English)In: Proceedings of 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 149-155Conference paper, Published paper (Refereed)
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 degree panoramic image around the USV. We design a 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 (DEM) data in the Fourier domain using a MOSSE 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 a field trial in the archipelago. Our approach provides promising results by achieving position estimates with GPS-level accuracy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 149-155
Keywords [en]
position estimation, horizon, registration, cnn, convolutional neural networks, cameras, correlation, Global Positioning System, sea measurements, digital elevation model, dem
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems) Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-161034DOI: 10.1109/IPAS.2018.8708868ISI: 000471844500026ISBN: 9781728102474 (electronic)ISBN: 9781728102467 (electronic)ISBN: 9781728102481 (print)OAI: oai:DiVA.org:liu-161034DiVA, id: diva2:1361978
Conference
2018 IEEE International Conference on Image Processing, Applications and Systems, December 12-14, 2018, Inria, Sophia Antipolis, France
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research , RIT 15-0097Swedish Research Council, 2016-05543
Note

Funding agencies:

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

This work was supported by the Swedish Foundation for Strategic Research (Smart Systems: RIT 15-0097).

This research is supported by CENIIT grant (18.14), and VR starting grant (2016-05543).

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-31Bibliographically approved

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Grelsson, BertilRobinson, AndreasFelsberg, MichaelKhan, Fahad Shahbaz
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