Deep Neural Networks for Ortophoto-Based Vehicle Localization

Alexander Rezanov, Dmitry Yudin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Navigation of unmanned vehicle especially using orthophoto is a topic of active research. This paper is dedicated to study of different methods of orthophoto-based localization methods. For this task new dataset was created. It consists of pairs of ground level and bird’s eye view images collected on vehicle test site of the technology contest Up Great “Winter City”. Different deep network approaches to localization were used: 1) embedding-based, 2) based on synthesis of bird’s eye view using Pix2pix conditional generative adversarial network and masked cross-correlation in map subwindow. The second approach has demonstrated good applicability for the proposed dataset. Mean absolute error of localization on known scenes reached 1 m. The average total time of bird’s eye view generation and subsequent localization is from 0.1 s to 0.2 s. This is an acceptable quality for the task solution and its further use as part of the navigation systems of unmanned vehicles.

Original languageEnglish
Title of host publicationAdvances in Neural Computation, Machine Learning, and Cognitive Research IV - Selected Papers from the 22nd International Conference on Neuroinformatics, 2020
EditorsBoris Kryzhanovsky, Witali Dunin-Barkowski, Vladimir Redko, Yury Tiumentsev
PublisherSpringer Science and Business Media Deutschland GmbH
Pages167-174
Number of pages8
ISBN (Print)9783030605766
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event22nd International Conference on Neuroinformatics, 2020 - Moscow, Russian Federation
Duration: 12 Oct 202016 Oct 2020

Publication series

NameStudies in Computational Intelligence
Volume925 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference22nd International Conference on Neuroinformatics, 2020
Country/TerritoryRussian Federation
CityMoscow
Period12/10/2016/10/20

Keywords

  • Cross-correlation
  • Deep learning
  • Generative adversarial network
  • Localization
  • Neural network
  • Orthophoto
  • Unmanned vehicle

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