Global Navigation Satellite Systems augmentation for mobile devices

在开发用于移动的设备的基于位置的服务（LBS）时，增强现实（AR）软件 ** 应用，Shapetrace Inc.发现用户的真实感受到基于移动设备的GPS定位不准确和不精确的限制。提高定位性能将允许Shapetrace的同步位置 ** 和映射（SLAM）算法，并将位置与图像识别功能强大地结合起来，以获得更逼真的户外AR体验。然而，市场上不存在独立的厘米到分米级别的移动终端 ** 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GNSS接收器和天线，以及昂贵的本地校正基础设施。该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，为移动的设备精确卫星定位这一商业要求提供一种解决办法。该引擎的元素 ** 将用于在Shapetrace** 云开发中设计高精度定位处理架构，并在城市环境中使用选定的移动的 ** 设备进行各种精度水平的可行性研究。解决方案将被设计为在城市峡谷中产生实时的m级水平（2 sigma）** 和cm级3D定位（1 sigma）。**预计Shapetrace客户端 ** 的高性能定位功能将通过其云服务器实现商业化。这一能力将提供一个竞争优势，预计将通过销售其现有的应用程序服务，并通过该公司的能力，提供 ** 显着更多和不同的AR体验和精确的LBS许多新客户增加 **Shapetrace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.