HorizonNet for visual terrain navigation

arXiv:2608.30471v1 Announce Type: cross Abstract: 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.

Abstract: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.

7 pages, 7 figures, 1 table. Published at IEEE IPAS 2018. An extended version appeared in Journal of Field Robotics 37(6):951-971, 2020, doi:https://doi.org/10.1002/rob.21929

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2608.30471 [cs.CV]

(or arXiv:2608.30471v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.30471 arXiv-issued DOI via DataCite (pending registration)

2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), pp. 149-155

https://doi.org/10.1109/IPAS.2018.8708868 DOI(s) linking to related resources

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HorizonNet for visual terrain navigation

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