Recently, indoor positioning has played a critical role in many emerging indoor applications. However, due to complicated indoor environments, it is still challenging to develop an indoor positioning system with high positioning accuracy and low deployment efforts. In this work, an indoor positioning system based on visible light fingerprinting is proposed by leveraging a novel visible light GraphSLAM (VL-GraphSLAM) technique. The proposed VL-GraphSLAM provides enhanced solutions at both frontend and backend to improve the accuracy of estimated trajectories. Then, the estimated trajectory is anchored in floor map based on a novel door detection method to recover indoor walking paths. Based on VL-GraphSLAM, we construct a database with the visible light received signal strength labeled by the locations of walking paths, which is called visible light map. Moreover, a Kalman filter is adopted to fuse the visible light fingerprinting and inertial sensors to locate users. Comprehensive experiments illustrate that our proposed system can accurately recover walking paths (0.4 m) and locate users (0.9 m) in an accuracy of submeter, which significantly outperforms a traditional WiFi-based fingerprinting system and is more convenient to deploy than a traditional visible light positioning based on ranging.

中文翻译：

---

通过可见光GraphSLAM技术增强和方便的室内定位

最近，室内定位在许多新兴的室内应用中发挥了关键作用。然而，由于复杂的室内环境，开发具有高定位精度和低部署努力的室内定位系统仍然是挑战。在这项工作中，利用新颖的可见光GraphSLAM（VL-GraphSLAM）技术，提出了一种基于可见光指纹识别的室内定位系统。拟议的VL-GraphSLAM在前端和后端都提供了增强的解决方案，以提高估计轨迹的准确性。然后，基于一种新颖的门检测方法，将估计的轨迹锚定在楼层地图中，以恢复室内步行路径。基于VL-GraphSLAM，我们构建了一个数据库，其中可见光接收信号强度由步行路径的位置标记，这就是所谓的可见光贴图。此外，采用卡尔曼滤波器融合可见光指纹和惯性传感器以定位用户。全面的实验表明，我们提出的系统可以在亚米级精度下准确地恢复步行路径（0.4 m）和定位用户（0.9 m），这明显优于传统的基于WiFi的指纹系统，并且比传统的可见光灯更易于部署根据测距进行定位。