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.

中文翻译：

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可见光定位的不连续指纹和大型实验数据集

本文提出了一种自动收集可见光定位（VLP）数据的方法，并使用大量实验数据对VLP进行了全面研究。使用一种新颖的方法可以有效地收集接收信号强度（RSS）数据，该方法利用了用户级虚拟现实（VR）跟踪来进行准确的地面真相记录。对地面真相系统的准确性进行的调查显示，中位数和第90个百分位误差分别为4.24和7.35毫米。将VR跟踪器与配备光电二极管的VLP接收器一起放置在移动机器人平台上，可以实现传统手动收集方法无法实现的大规模和精确指纹识别。在装有11个VLP照明灯的6.3×6.9 m测试空间内的7344个位置的RSS数据已收集，可供研究人员使用。数据的质量和数量允许使用可见光对基于机器学习（ML）和基于通道模型的定位进行可靠的研究。在基于ML的技术中，发现岭回归是最准确的，优于加权k最近邻，多层感知器和随机森林等。当少量数据集可用于校准和训练时，基于模型的定位比ML技术更准确。但是，如果有大量数据可用于训练，则基于ML的定位在定位精度方面将优于其基于模型的定位。胜过加权k最近邻，多层感知器和随机森林等。当少量数据集可用于校准和训练时，基于模型的定位比ML技术更准确。但是，如果有大量数据可用于训练，则基于ML的定位在定位精度方面将优于其基于模型的定位。胜过加权k最近邻，多层感知器和随机森林等。当少量数据集可用于校准和训练时，基于模型的定位比ML技术更准确。但是，如果有大量数据可用于训练，则基于ML的定位在定位精度方面将优于其基于模型的定位。