A Biologically Inspired Architecture for Visual Self-location

  • Helio Perroni Filho
  • Akihisa Ohya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 449)


Self-location—recognizing one’s surroundings and reliably keeping track of current position relative to a known environment—is a fundamental cognitive skill for entities biological and artificial alike. At a minimum, it requires the ability to match current sensory (mainly visual) inputs to memories of previously visited places, and to correlate perceptual changes to physical movement. Both tasks are complicated by variations such as light source changes and the presence of moving obstacles. This article presents the Difference Image Correspondence Hierarchy (DICH), a biologically inspired architecture for enabling self-location in mobile robots. Experiments demonstrate DICH works effectively despite varying environment conditions.


Mobile Robot Difference Image Cosine Similarity Shift Vector Walk Away 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (grant 201799/2012-0).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Intelligent Robot LaboratoryUniversity of TsukubaTsukubaJapan

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