Paper
4 March 2022 Indoor visual mapping and navigation for blind people
Darius Plikynas
Author Affiliations +
Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120840S (2022) https://doi.org/10.1117/12.2623893
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy
Abstract
Admittedly, machine vision-based assistive applications are beneficial for blind and visually impaired (BVI) persons. Such a need has numerous already implemented outdoor assistive solutions. However, there are much less effective solutions for indoor navigation and orientation. It is due to the absence of GPS signals and the need for infrastructural investments (such as WI-FI signals, beamers, RFID tags). In this paper, we present another way - a wearable electronic traveling aid (ETA) system for the BVI persons using outsourcing, i.e., volunteers’ mapping of buildings indoor routes. Volunteers use the proposed wearable ETA device to record indoor routes stored in the web cloud database using web services. Smartphones’ IMU and other sensors, stereo and depth camera, audio and haptic devices, computer vision algorithms, and computational intelligence are employed for objects detection and recognition, and consequently, intelligent routing and mapping of indoor spaces. Integration of semantic data of points of interest (such as stairs, doors, WC, entrances/exits) and building (evacuation) schemes makes the proposed approach even more attractive to the BVI users. The presented approach can also be employed to crowdsourcing real-time help in complex navigational situations such as dead reckoning, avoiding various obstacles, or unforeseen situations.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Darius Plikynas "Indoor visual mapping and navigation for blind people", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120840S (4 March 2022); https://doi.org/10.1117/12.2623893
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KEYWORDS
Cameras

Navigation systems

Clouds

Sensors

Buildings

Visualization

Machine learning

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