Robust Device Position and Pose Detection Using Visible Light without Model Knowledge: A Branch-Structured Residual Learning Method

Jieyou Zhu, Bingpeng Zhou, Xin Wang, Xinghua Sun, Hongyang Chen

Abstract: In this paper, we focus on visible light-based position and pose detection (VLP) for user devices in dynamical environments. Traditional model-based VLP methods usually depend on a perfect signal propagation model (SPM) with fixed parameters, and hence their performance will be seriously decreased when localization environment varies over time, e.g., due to diffuse scattering and reflections. To address this challenge, in this paper we propose a novel branch-structured residual convolutional neural network (RCNN)-based VLP method, without any requirement on perfect SPM knowledge. We observe that there are environment-invariant texture features in received visible light signal samples, which can be exploited for VLP performance enhancement. A branch-structured RCNN-based VLP scheme is devised for exploiting diverse-level stable texture features from received measurement samples, rendering a reliable VLP solution against environmental dynamics. It is verified by simulations that our branch-structured RCNN-based VLP solution outperforms existing machine learning-based VLP methods.