Guided Sonar-to-Satellite Translation

Abstract

Underwater navigation and localization are greatly enhanced by the use of acoustic images. However, such images are of difficult interpretation. Contrarily, aerial images are easier to interpret, but require Global Positioning System (GPS) sensors. Due to absorption phenomena, GPS sensors are unavailable in underwater environments. Thus, we propose a method to translate sonar images acquired underwater to an aerial counterpart. This process is called sonar-to-satellite translation. To perform the conversion, a U-Net based neural network is proposed, enhanced with state-of-the-art techniques, such as dilated convolutions and guided filters. Afterwards, our approach is validated on two datasets containing sonar images and their satellite analogue. Qualitative experimental results indicate that the proposed method can transfer features from acoustic images to aerial images, generating satellite images that are easier to interpret and visualize.

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Availability of data and materials

TensorFlow implementation of the model and the ARACATI 2017 dataset are available for download at: https://github.com/giovgiac/son2sat.

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Acknowledgements

This research is partly supported by CNPq, CAPES and FAPERGS. We also would like to thank the colleagues from NAUTEC-FURG for helping with the experimental data and for productive discussions and meetings. Finally, we would like to thank NVIDIA for donating high-performance graphics cards. All authors are with NAUTEC, Intelligent Robotics and Automation Group, Universidade Federal do Rio Grande - FURG, Rio Grande - Brazil.

Author Contributions

  • Giovanni G. De Giacomo: implementation and execution of the Deep Learning experiments; writing of the manuscript.

  • Matheus M. dos Santos: development of the dataset and associated tools; helped writing the manuscript.

  • Paulo L. J. Drews-Jr: theoretical support on the idea; revising the manuscript.

  • Silvia S. C. Botelho: theoretical support on the idea; revising the manuscript.

Funding

This study was partly supported by the National Council for Scientific and Technological Development (CNPq) and Coordenacao de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001. This paper is also a contribution of the INCT-Mar COI funded by CNPq Grant Number 610012/2011-8.

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Correspondence to Giovanni G. De Giacomo.

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De Giacomo, G.G., dos Santos, M.M., Drews, P.L.J. et al. Guided Sonar-to-Satellite Translation. J Intell Robot Syst 101, 46 (2021). https://doi.org/10.1007/s10846-021-01324-2

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Keywords

  • Deep learning
  • Neural networks
  • Robotics