Global Navigation Satellite Systems augmentation for mobile devices

在为移动设备开发基于位置的服务(LBS)时，例如增强现实(AR)软件**应用程序，Shapetrace Inc.发现，基于移动设备的GPS定位**不准确和不精确限制了用户的真实感。改进的定位性能将允许Shapetrace的同时定位**和映射(SLAM)算法，并将定位与图像识别功能牢固地绑定在一起，以获得更逼真的**室外AR体验。然而，市场上没有独立的、厘米到分米级别的移动设备**定位能力。为了实现如此高的性能，现有的商业**解决方案需要使用昂贵的全球导航卫星系统接收器和天线以及昂贵的本地校正基础设施。**本项目旨在应用约克大学全球导航卫星系统实验室**开发的全球导航卫星系统测量处理引擎(一种方法和软件)进行高性能定位和导航，为移动设备精确的卫星定位这一商业需求提供**解决方案。该引擎的元素**将用于设计Shapetrace**云开发中的高精度定位处理架构，并在城市环境中使用选定的移动**设备执行各种精度水平的可行性研究。解决方案将设计为在城市峡谷中产生实时的m级水平(2西格玛)**和厘米级3D定位(1西格玛)。**预计高性能定位能力将通过其云服务器为Shapetrace客户**进行商业化。这一能力将提供一个具有竞争力的差异化优势，预计将通过销售其现有的应用程序服务，以及该公司向许多新客户提供**显著更多和多样化的AR体验和精确的LBS的能力，来增加**Shapetrace的收入。

In developing location-based services (LBS) for mobile devices, e.g., augmented reality (AR) software**applications, Shapetrace Inc. has found that user realism is limited by mobile-device-based GPS positioning**inaccuracy and imprecision. Improving positioning performance will allow Shapetrace's simultaneous location**and mapping (SLAM) algorithms and robustly bond location to image recognition features for a more realistic**outdoor AR experience. However, there is no stand-alone, centimetre-to-decimetre-level mobile device**positioning capability in the marketplace. In order to achieve such high performance, existing commercial**solutions require the use of expensive GNSS receivers and antennas, and costly local correction infrastructure.**This project aims to apply a GNSS measurement processing engine (a methodology and software) developed**by the GNSS Laboratory at York University for high-performance positioning and navigation to provide a**solution for this commercial requirement of accurate satellite-based positioning for mobile devices. Elements**of the engine will be used to design high-accuracy positioning processing architectures within the Shapetrace**cloud development, and to perform feasibility studies for various levels of accuracy with selected mobile**devices in urban environments. Solutions will be designed to produce real-time, m-level horizontal (2 sigma)**and cm-level 3D positioning (1 sigma) in urban canyons.**It is expected that the high-performance positioning capability will be commercialized for Shapetrace clients**through its cloud server. This capability will provide a competitive differentiator that is expected to increase**Shapetrace revenue through sales of its existing app services, and through the company's ability to provide**significantly more and varied AR experiences and precise LBS to many new clients.