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Avision-based indoor navigation system for individuals with visual impairment
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
Mälardalens högskola, Västerås, Sweden.
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2019 (English)In: International Journal of Artificial Intelligence, ISSN 0974-0635, E-ISSN 0974-0635, Vol. 17, no 2, p. 188-201Article in journal (Refereed) Published
Abstract [en]

Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.

Place, publisher, year, edition, pages
CESER Publications , 2019. Vol. 17, no 2, p. 188-201
Keywords [en]
Deep learning, Depth estimation, Indoor navigation, Object detection, Object recognition
National Category
Robotics Computer Sciences Computer Systems Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:mdh:diva-45835Scopus ID: 2-s2.0-85073347243OAI: oai:DiVA.org:mdh-45835DiVA, id: diva2:1365489
Available from: 2019-10-25 Created: 2019-10-25 Last updated: 2019-10-25

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Ahmed, Mobyen UddinAltarabichi, Mohammed GhaithBegum, ShahinaRahman, Hamidur

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Ahmed, Mobyen UddinAltarabichi, Mohammed GhaithBegum, ShahinaRahman, Hamidur
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RoboticsComputer SciencesComputer SystemsComputer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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