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.

中文翻译：

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一种基于云的精确低延迟室内定位系统

室内定位系统最近变得越来越流行。虽然大多数现有的室内定位研究都集中在提高准确性上，但对延迟问题的关注较少。传统的融合算法利用基于接收信号强度（RSS-Based）的定位结果来修正系统的当前位置。但是，在导航过程中却跟不上用户的实际移动速度。本文提出的位置追溯调整(LRA)方法利用RSS-Based定位结果对系统的过去位置进行校正，可以有效消除定位延迟，提高导航的实时性。我们在 70 m 的线性长廊中进行了测试，发现添加 LRA 导致定位误差减少了 -0.3 到 +0.4 m 左右，提高了 85%。此外，LRA方法减轻了对RSS定位即时响应的要求，RSS定位算法可以上云。它减少了手机的下载资源和计算负载。完整的室内导航应用以 HTML5 呈现，用户无需提前下载 APP 即可导航，用户首次启动应用仅需 4-9 秒。我们在总建筑面积为 79,000 m 的医院测试了该应用程序 完整的室内导航应用以 HTML5 呈现，用户无需提前下载 APP 即可导航，用户首次启动应用仅需 4-9 秒。我们在总建筑面积为 79,000 m 的医院测试了该应用程序 完整的室内导航应用以 HTML5 呈现，用户无需提前下载 APP 即可导航，用户首次启动应用仅需 4-9 秒。我们在总建筑面积为 79,000 m 的医院测试了该应用程序^{7 栋建筑中有2}栋。该系统在220 m的长导航距离上实现了0.65 m的平均定位精度。据我们所知，本文是第一个考虑室内导航延迟问题的论文。所提出的 LRA 方法提高了实时导航性能，减轻了手机的计算负载，并使基于云的定位系统即使在拥挤地区网络质量较差的情况下也能提供稳定准确的导航。