Abstract

The benefits of technologies related to the Internet of Things (IoT), virtual and augmented reality (VR/AR), digital twins, and so on, can be fully realized when associated devices are positioned intuitively. However, AR systems hosted within smartphones pose challenges where auxiliary hardware and computational configurations associated with precise positioning are concerned. To this effect, we propose a deep learning-based indoor measurement system that can determine positions using images collected via beacons designed as IoT terminals. The proposed system is broadly divided into a detection unit, an extraction unit, a positioning unit, and a management server. The beacons were detected using deep learning algorithms, from which the postures were extracted using a homography matrix, and position of the imaging device was determined in reference to the beacon’s position. With the unique design of our system, in that it simultaneously performs posture and positioning estimations, high immersive AR can be achieved. Moreover, scalability of the positioning space is also guaranteed as multiple beacons can be monitored at once. For the experiment, we simulated a virtual indoor space comprising pyramid beacons and the results were promising.

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