Calibration of Visible Light Positioning Systems with a Mobile Robot

Abstract

Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration.

Automating the Calibration of Visible Light Positioning Systems

“…Simulations are an appropriate evaluation tool in this case as we are mostly interested in the relative performance of each method. The accuracy of robot calibration with manual steering was already evaluated in [ 8 , 9 ]. This work will use the same manual calibration method as a point of reference for the automated methods.…”

“…As mentioned in Section 1 , the parameters that need to be calibrated depend on the positioning technique and hence influence the calibration of these systems. For an in-depth overview of calibration methods suited for different types of positioning techniques, the reader is referred to our previous work [ 9 ]. However, the main distinction in calibration methods is between the calibration of range-based and fingerprinting systems.…”

“…Additionally, the resulting positioning accuracy is relatively low (0.8 m and 2.30 m on average, respectively). Our previous works [ 8 , 9 ] showed that calibration (and subsequent positioning) accuracy can be very high if a mobile robot is used for calibration. However, those works also required a human operator to steer the robot.…”

“…However, despite the popularity of visible light positioning, calibration of these systems is rarely discussed. In our previous work [ 8 , 9 ], we proposed the use of a mobile robot to collect the necessary calibration data for a VLP system. In postprocessing, these data could be used to reconstruct a map of the environment with the light source locations and their identifiers.…”

Indoor Positioning and Navigation

“…In the field of indoor localization, visible light positioning (VLP) systems are also very promising. Authors Amsters et al [ 16 ] presented an innovative solution for calibrating VLP systems using a mobile robot to facilitate data acquisition. The new approach significantly improved performance compared to previous studies, almost doubling the accuracy of LED localization.…”