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Details of Grant 

EPSRC Reference: EP/L018446/1
Title: Intelligent Positioning in Cities using GNSS and Enhanced 3D Mapping
Principal Investigator: Groves, Dr P D
Other Investigators:
Ellul, Dr C
Researcher Co-Investigators:
Project Partners:
Ordnance Survey Royal National Inst of Blind People RNIB S T Microelectronics
Department: Civil Environmental and Geomatic Eng
Organisation: UCL
Scheme: Standard Research
Starts: 02 June 2014 Ends: 01 June 2018 Value (£): 361,097
EPSRC Research Topic Classifications:
RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles Electronics
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Feb 2014 EPSRC ICT Responsive Mode - Feb 2014 Announced
Summary on Grant Application Form
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.

Key Findings
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