Abstract
This paper presents an autonomous method of collecting data for Visible Light Positioning (VLP) and a comprehensive investigation of VLP using a large set of experimental data. Received Signal Strength (RSS) data are efficiently collected using a novel method that utilizes consumer grade Virtual Reality (VR) tracking for accurate ground truth recording. An investigation into the accuracy of the ground truth system showed median and 90th percentile errors of 4.24 and 7.35 mm, respectively. Co-locating a VR tracker with a photodiode-equipped VLP receiver on a mobile robotic platform allows fingerprinting on a scale and accuracy that has not been possible with traditional manual collection methods. RSS data at 7344 locations within a 6.3 × 6.9 m test space fitted with 11 VLP luminaires is collected and has been made available for researchers. The quality and the volume of the data allow for a robust study of Machine Learning (ML)- and channel model-based positioning utilizing visible light. Among the ML-based techniques, ridge regression is found to be the most accurate, outperforming Weighted k Nearest Neighbor, Multilayer Perceptron, and random forest, among others. Model-based positioning is more accurate than ML techniques when a small data set is available for calibration and training. However, if a large data set is available for training, ML-based positioning outperforms its model-based counterparts in terms of localization accuracy.
Highlights
Localization and navigation systems have been widely used since the development of Global Positioning System (GPS) [1]
A large number of potential applications such as smart guidance in large facilities, e.g., hospitals and shopping malls, ambient assisted living in smart homes, and asset tracking have led researchers to develop Indoor Positioning System (IPS) using a wide variety of techniques including Infrared (IR) [3], Wi-Fi [4], Bluetooth [5], Zigbee [6], ultra-wideband (UWB) [7], acoustics [8], and computer vision systems [9]
Fingerprint techniques, on the other hand, capture a labeled offline data set of locations and corresponding Received Signal Strength (RSS) of multiple luminaires and use this to build a machine learning algorithm capable of predicting online locations
Summary
Localization and navigation systems have been widely used since the development of Global Positioning System (GPS) [1]. Fingerprint techniques, on the other hand, capture a labeled offline data set of locations and corresponding RSS of multiple luminaires and use this to build a machine learning algorithm capable of predicting online locations. For both approaches, there is a need for a large number of experimental measurements of RSS and the positions at which they were taken with high confidence in accuracy. The painstaking fashion of the method can introduce error as user fatigue develops over long sessions of data collection This has resulted in VLP research being conducted with data sets of limited size. Alam et al [17] Guo et al [18] Vongkulbhisal et al [19] Zhang et al [20] Zhang et al [21] Chuang et al [22] Wu et al [23]
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