scholarly journals An Indoor and Outdoor Navigation System for Visually Impaired People

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 170406-170418 ◽  
Author(s):  
Daniele Croce ◽  
Laura Giarre ◽  
Federica Pascucci ◽  
Ilenia Tinnirello ◽  
Giovanni Ettore Galioto ◽  
...  
Keyword(s):  
Navigation System ◽  
Visually Impaired ◽  
Visually Impaired People ◽  
Impaired People ◽  
Indoor And Outdoor ◽  
Outdoor Navigation

scholarly journals A Navigation and Augmented Reality System for Visually Impaired People

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3061
Author(s):  
Alice Lo Valvo ◽  
Daniele Croce ◽  
Domenico Garlisi ◽  
Fabrizio Giuliano ◽  
Laura Giarré ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Visually Impaired ◽  
Virtual Path ◽  
Physical Support ◽  
Augmented Reality System ◽  
Virtual Paths ◽  
Visually Impaired People ◽  
Impaired People ◽  
Autonomous Mobility ◽  
Indoor And Outdoor

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.

scholarly journals Wearable Travel Aid for Environment Perception and Navigation of Visually Impaired People

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 697 ◽  
Author(s):  
Jinqiang Bai ◽  
Zhaoxiang Liu ◽  
Yimin Lin ◽  
Ye Li ◽  
Shiguo Lian ◽  
...  
Keyword(s):  
Visually Impaired ◽  
Social Inclusion ◽  
Speech Synthesis ◽  
Semantic Information ◽  
Assistive Device ◽  
Measurement Unit ◽  
Human Machine Interaction ◽  
Visually Impaired People ◽  
Impaired People ◽  
Indoor And Outdoor

Assistive devices for visually impaired people (VIP) which support daily traveling and improve social inclusion are developing fast. Most of them try to solve the problem of navigation or obstacle avoidance, and other works focus on helping VIP to recognize their surrounding objects. However, very few of them couple both capabilities (i.e., navigation and recognition). Aiming at the above needs, this paper presents a wearable assistive device that allows VIP to (i) navigate safely and quickly in unfamiliar environment, and (ii) to recognize the objects in both indoor and outdoor environments. The device consists of a consumer Red, Green, Blue and Depth (RGB-D) camera and an Inertial Measurement Unit (IMU), which are mounted on a pair of eyeglasses, and a smartphone. The device leverages the ground height continuity among adjacent image frames to segment the ground accurately and rapidly, and then search the moving direction according to the ground. A lightweight Convolutional Neural Network (CNN)-based object recognition system is developed and deployed on the smartphone to increase the perception ability of VIP and promote the navigation system. It can provide the semantic information of surroundings, such as the categories, locations, and orientations of objects. Human–machine interaction is performed through audio module (a beeping sound for obstacle alert, speech recognition for understanding the user commands, and speech synthesis for expressing semantic information of surroundings). We evaluated the performance of the proposed system through many experiments conducted in both indoor and outdoor scenarios, demonstrating the efficiency and safety of the proposed assistive system.

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