An Efficient Wi-Fi–Vision Map Construction and Self-Maintenance Method for Indoor Localization
Chenjun Tang, Wei Sun, Xing Zhang, Jin Zheng, Kailong Li, Jian Liu
Abstract
Currently, Wi-Fi-based indoor localization methods have been proven to be promising due to their low deployment cost. However, the overhead of constructing and maintaining maps remains a bottleneck for the widespread deployment of Wi-Fi-based indoor localization methods. In this article, we propose a novel combined Wi-Fi and vision to construct and maintain maps. This method consists of three parts (including constructing the logarithmic distance path loss (LDPL) model using an improved whale optimization algorithm (IWOA), a novel fusion localization module (called LDPL-PF), and a lightweight threshold-based map maintenance model). Specifically, the LDPL model based on IWOA can first construct high-quality maps with limited data. Then, LDPL-PF localization method is used to determine the user’s location based on the map. Finally, a feedback mechanism is introduced to achieve map maintenance automatically. The localization results and the collected data are fed into a multidecision mechanism to build a feedback network for long-term map maintenance. Extensive experimental results show that our proposed method has good accuracy and stability with state-of-the-art methods.