Precise Local Positioning of a Mobile Device Based on Pose Reconstruction From a Visible Light Beacon

This paper proposes an indoor positioning system for mobile devices using visible light beacons. The device’s camera is used to acquire images in which the beacon appears. An algorithm processes these images to reconstruct the camera’s pose at the photo acquisition time. This reconstruction allows for estimating the camera position accurately. The system operation is tested by a simulator based on Blender. This simulator allows for setting the beacon and camera characteristics, taking samples in rooms of different dimensions and analysing and studying the results obtained. In addition, an Android application for the real system has been developed to take samples and analyse them to estimate the mobile device’s position. Finally, a comparison between the real and simulated systems is made. For this purpose, a test bench grid of
$\mathrm {0.8\,\, \text {m}}\,\,\times \,\,\mathrm {1.1\,\, \text {m}}$
with 391 test points is designed. The simulator offers an average fidelity 97% and can automate the sampling process. An average error of
$\mathrm {7.49e-3\,\, \text {m}}$
and coverage of 100% are achieved with the camera and LED panel facing each other. The test bench real system achieves an average positioning error of less than
$\mathrm {20.44e-3\,\, \text {m}}$
, having a coverage close to 80% and decreasing performance when the beacons are not fully captured. Finally, an experimental study of the system scalability has been carried out in a test room where four coded beacons have been deployed covering an area of
$2.4\times \,\,\mathrm {3.6\,\, \text {m}}^{2}$
. Results over two different trajectories show reasonable losses of accuracy and coverage compared to the simulation and test bench, especially in the transition between beacons.