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Avision-based indoor navigation system for individuals with visual impairment
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-3802-4721
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-1212-7637
Mälardalens högskola, Västerås, Sweden.
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2019 (Engelska)Ingår i: International Journal of Artificial Intelligence, ISSN 0974-0635, E-ISSN 0974-0635, Vol. 17, nr 2, s. 188-201Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
CESER Publications , 2019. Vol. 17, nr 2, s. 188-201
Nyckelord [en]
Deep learning, Depth estimation, Indoor navigation, Object detection, Object recognition
Nationell ämneskategori
Robotteknik och automation Datavetenskap (datalogi) Datorsystem Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:mdh:diva-45835Scopus ID: 2-s2.0-85073347243OAI: oai:DiVA.org:mdh-45835DiVA, id: diva2:1365489
Tillgänglig från: 2019-10-25 Skapad: 2019-10-25 Senast uppdaterad: 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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International Journal of Artificial Intelligence
Robotteknik och automationDatavetenskap (datalogi)DatorsystemDatorseende och robotik (autonoma system)

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