Combining Visual Based Localization with Received Signal Indicator for an Augmented Reality Indoors Navigation
Combining Visual Based Localization with Received Signal Indicator for an Augmented Reality Indoors Navigation
Date
2021-05
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Authors
Aduko, Faisal Wahabu
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Abstract
Indoor navigation has recently become an important topic due to the rampant
development of complex buildings that make navigation within those structures difficult. The
Global Navigational Satelite Systems(GSS), like the Global Positioning System(GPS),
currently cannot provide navigation services to indoor environments since they require a direct
line of sight between the satellites and the receiving devices. Hence, an indoor
positioning system is needed to serve as the GPS equivalent for indoor environments. There are
different indoor positioning technologies, each with advantages and shortfalls. This
project explored the idea of sensor fusion where two positioning methods; the Received Signal
The strength Indicator(RSSI) of WiFi and the Visual Based Localisation methods are combined to
implement an accurate and more reliable indoor positioning system with an augmented reality
navigation experience. The Google ARCore SDK with Simultaneous Visual Inertial
Odometry algorithms integrated into it was used to implement the visual base localization,
while the Root Mean Square Error algorithm was used to implement the RSSI method. Testing
the system on different indoor lighting conditions showed an average deviation of 16.73 cm
from the actual position. The results indicated that the lighting condition of an indoor space
impacts the Localization of a navigating user in the indoor environment.
Description
Capstone Project submitted to the Department of Engineering, Ashesi University College in partial fulfillment of the requirements for the award of a Bachelor of Science degree in Electrical and Electronics Engineering.
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Capstone project
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Citation
Wahabu, F. A. (2021). Combinging Visual Based Localization wiht Received Signal Strength Indicator for and Augmented Reality Indoor Navigation61pgs.