@inbook{10.1145/3460418.3480406,
author = {Su, Danping and Liu, Xianbin and Liu, Sicong},
title = {Three-Dimensional Indoor Visible Light Localization: A Learning-Based Approach},
year = {2021},
isbn = {9781450384612},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3460418.3480406},
abstract = { In this paper, a three-dimensional (3D) indoor visible light localization method based on machine learning and deep learning is presented, which is able to obtain accurate 3D spatial coordinates of the user, including the location on the plane and the height in a room. The machine learning approaches adopted for localization include two typical algorithms, i.e., support vector machine and random forest. For the deep learning based approach, deep neural networks composed of full connected layers are employed for training in different indoor visible light localization scenarios. In the formulated learning-based visible light localization framework, the received signal strength of light-emitting diodes are taken as the input of the learning algorithm, and the measured position coordinates are inferred as the output. Apart from obtaining the two-dimensional location on the plane accurately, we also take the height into account and accurate 3D coordinates with height are obtained. The experimental results show that centimeter-scale accuracy of 3D indoor localization can be achieved using the proposed learning-based visible light localization method. Moreover, the performance of the visible light localization methods with respect to the number and the spatial pattern of LEDs, and the number of neural network layers, are also investigated.},
booktitle = {Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
pages = {672–677},
numpages = {6}
}