Intelligent Positioning in Cities using GNSS and Enhanced 3D Mapping

使用 GNSS 和增强型 3D 测绘在城市中进行智能定位

• 批准号: EP/L018446/1
• 负责人: Paul Groves
• 金额: $ 46.01万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL018446%2F1
• 关键词: Intelligent; Positioning; Cities; using; GNSS

Poor positioning performance in dense urban areas is a major obstacle to the practical realisation of new technologies such as navigation for the visually impaired, tracking people with chronic medical conditions, augmented reality, advanced lane control systems for vehicles and advanced railway signalling systems.The Global Positioning System (GPS) provides metres-level positioning in open environments. However in dense urban areas, buildings block, attenuate, reflect and diffract radio signals, limiting the real-time positioning accuracy to 10-50m when enough signals can be received to calculate a position. Other radio positioning technologies are typically no more accurate, while position obtained from dead reckoning degrades with time. Optical techniques developed by the robotics community are more suited to some applications than others and are still undergoing research to make them more reliable and efficient.Using the new global navigation satellite systems (GNSS) constellations (i.e., GLONASS and, in future, Galileo and Compass) in addition to GPS improves the availability of satellite-based positioning in urban areas. However, to improve the accuracy, a new approach to positioning is needed and the increasing availability of 3D mapping provides an opportunity to achieve this. The aim of this project is thus to improve the accuracy of real-time mobile positioning in urban areas to within a few metres by combining multi-constellation GNSS with 3D mapping, a concept known as intelligent urban positioning. By exploiting knowledge of the surroundings provided by 3D city models and rebuilding the positioning algorithms from the bottom up to make use of all available information, a step change in positioning performance can be achieved, unlocking the potential for a host of new positioning applications.This research will build on UCL's track record of innovation in urban positioning, including the development of a brand new GNSS positioning method known as shadow matching. This project will investigate new ways of using 3D mapping to aid ranging-based GNSS positioning and then combine this with shadow matching to obtain the best overall position solution. Testing will be conducted under a wide range of scenarios to assess how the performance varies as a function of the urban environment, the class of GNSS user equipment used and the characteristics of the 3D mapping. Finally, context detection algorithms will be developed to determine when the positioning system is in an environment suitable for the algorithms developed under this project and when it is in an environment where conventional GNSS algorithms or an indoor positioning technique should be deployed instead.By improving the accuracy and reliability of urban positioning, a successful outcome of this project would unlock the potential for many new applications that can both contribute to the economy and provide solutions to societal problems, while improving the reliability of many existing technologies. Positioning technology that can determine the correct side of the street and identify adjacent buildings is a key component of automated guidance for visually impaired pedestrians. More accurate emergency caller location and tracking of people with chronic medical conditions enables response teams to arrive more quickly. Augmented reality will benefit from a more efficient overlaying of information on the surrounding environment. Researchers mapping patterns of air pollution or wheelchair accessibility in cities will be able to quickly and cheaply geolocate information to within a few metres. More reliable identification of traffic lanes and railway tracks will support the development of advanced intelligent transport systems. Route guidance for visitors to cities, location of friends and business associates in complex or crowded urban environments, and location-based advertising will also benefit.

在人口密集的城市地区，定位性能差是实际实现新技术的主要障碍，这些新技术包括视障人士导航、跟踪慢性病患者、增强现实、先进的车辆车道控制系统和先进的铁路信号系统。全球定位系统（GPS）在开放环境中提供米级定位。然而，在密集的城市地区，建筑物阻挡、衰减、反射和干扰无线电信号，当可以接收到足够的信号来计算位置时，将实时定位精度限制在10- 50米。其他无线电定位技术通常并不更准确，而从航位推算获得的位置随着时间而退化。由机器人领域开发的光学技术比其他技术更适合于某些应用，并且仍在进行研究，以使其更加可靠和高效。除了全球定位系统之外，全球轨道导航卫星系统以及今后的伽利略和指南针系统改进了城市地区卫星定位的可用性。然而，为了提高精度，需要一种新的定位方法，而3D映射的日益可用性为实现这一目标提供了机会。因此，该项目的目的是通过将多星座全球导航卫星系统与三维测绘相结合，将城市地区实时移动的定位的准确度提高到几米以内，这一概念被称为智能城市定位。通过利用3D城市模型提供的周围环境知识，并自下而上重建定位算法，以利用所有可用信息，可以实现定位性能的阶跃变化，从而释放出大量新定位应用的潜力。这项研究将建立在UCL在城市定位方面的创新记录之上，包括开发一种称为阴影匹配的全新GNSS定位方法。该项目将研究使用3D映射来辅助基于测距的GNSS定位的新方法，然后将其与阴影匹配联合收割机相结合，以获得最佳的整体位置解决方案。将在各种设想方案下进行测试，以评估性能如何随城市环境、所用全球导航卫星系统用户设备的类别和三维绘图的特点而变化。最后，将开发上下文检测算法，以确定定位系统何时处于适合本项目下开发的算法的环境中，以及何时处于应部署传统GNSS算法或室内定位技术的环境中。通过提高城市定位的准确性和可靠性，这一项目的成功结果将释放许多新应用的潜力，这些应用既能促进经济，又能为社会问题提供解决办法，同时提高许多现有技术的可靠性。定位技术可以确定街道的正确一侧并识别相邻建筑物，是视障行人自动引导的关键组成部分。更准确的紧急呼叫者定位和跟踪慢性疾病患者，使响应团队能够更快地到达。增强现实将受益于更有效地覆盖周围环境的信息。研究人员绘制城市空气污染或轮椅无障碍模式的地图，将能够快速、廉价地将信息定位到几米之内。更可靠地识别行车道和铁路轨道将有助于发展先进的智能运输系统。为城市游客提供的路线指导，在复杂或拥挤的城市环境中朋友和商业伙伴的位置，以及基于位置的广告也将受益。

项目成果

GNSS Shadow Matching: The Challenges Ahead

• 发表时间: 2015-09
• 作者: P. Groves;Lei Wang;M. Adjrad;C. Ellul

Real-Time 3D Mapping Aided GNSS on and Android Devices

Android 设备上的实时 3D 地图辅助 GNSS

• DOI: 10.33012/2018.15884
• 发表时间: 2018
• 作者: Adjrad M

3D-mapping-aided GNSS exploiting Galileo for better accuracy in dense urban environments

3D 地图辅助 GNSS 利用 Galileo 在密集的城市环境中提高精度

• DOI: 10.1109/euronav.2017.7954199
• 发表时间: 2017
• 作者: Adjrad M

Enhancing Micro Air Vehicle Navigation in Dense Urban Areas using 3D Mapping Aided GNSS

使用 3D 绘图辅助 GNSS 增强密集城市地区的微型飞行器导航

• DOI: 10.33012/2017.15211
• 发表时间: 2017
• 作者: Groves P

It’s Time for 3D Mapping–Aided GNSS

是时候进行 3D 测绘辅助 GNSS 了

• 发表时间: 2016
• 作者: P. Groves