An accurate cloud‐based indoor localization system with low latency

Abstract

Indoor positioning systems are becoming increasing popular recently. While most existing indoor positioning studies focus on improving accuracy, less attention is paid to the latency problem. The traditional fusion algorithm uses the received signal strength-based (RSS-Based) positioning result to correct the current position of the system. However, it fails to keep up with the actual moving speed of the user during the navigation. The location retrospective adjustment (LRA) method proposed in this paper uses the RSS-Based positioning result to correct the past position of the system, which can effectively eliminate positioning delay and improve the real-time response of navigation. We tested in a 70 m linear promenade and found that the addition of LRA results in a reduction of the positioning error around −0.3 to +0.4 m, which improves 85%. Additionally, the LRA method alleviates the requirements for the immediate response of RSS positioning, and the RSS positioning algorithm can be moved to the cloud. It reduces the download resources and computing load on the mobile phone. The complete indoor navigation application is presented in HTML5 which allows users to navigate without having to download the APP in advance, and it takes only 4–9 s for users to launch the application for the first time. We tested the application in a hospital with a total floor area of 79,000 m2 in 7 buildings. The system achieves an average positioning accuracy of 0.65 m at a long navigation distance of 220 m. To our knowledge, this paper is the first to consider the latency issue in indoor navigation. The proposed LRA approach improves real-time navigation performance, lightens the computation load on the mobile phone, and allows cloud-based positioning systems to provide stable and accurate navigation even under poor network quality in crowded areas.