Navigation is one of the most complex daily activities we engage in. Partly due to its complexity, navigational abilities are vulnerable to many conditions including Topographical Agnosia, Alzheimer’s Disease, and vision impairments. While navigation using solely vision remains a difficult problem in the field of assistive technology, emerging methods in Deep Reinforcement Learning and Computer Vision show promise in producing vision-based navigational aids for those with navigation impairments. To this effect, we introduce GraphMem, a Neural Computing approach to navigation tasks and compare it to several state of the art Neural Computing methods in a one-shot, 3D, first-person maze solving task. Comparing GraphMem to current methods in navigation tasks unveils insights into navigation and represents a first step towards employing these emerging techniques in navigational assistive technology.

Original languageEnglish (US)
Title of host publicationSmart Multimedia - 1st International Conference, ICSM 2018, Revised Selected Papers
EditorsStefano Berretti, Anup Basu
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030043742
StatePublished - 2018
Event1st International Conference on Smart Multimedia, ICSM 2018 - Toulon, France
Duration: Aug 24 2018Aug 26 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11010 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Conference on Smart Multimedia, ICSM 2018


  • Assistive technology
  • Navigation
  • Reinforcement learning
  • Topographical agnosia

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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