Updating Integrated GPS/INS Systems with Map Matching for Car Navigation Applications

M. Attia, A. Moussa, and N. El-Sheimy

Abstract:	Determining the location of a vehicle relative to the physical environment (e.g. roadway, services) is an important information for a wide range of transportation services, for example in-vehicle navigation, fleet management and infrastructure maintenance. The first step in vehicle location is determining the vehicle’s position in a reference coordinate system. Unfortunately, most of the available positioning technologies have limitations either in accuracy of the absolute position, accumulated error, cost, or availability. In order to overcome these limitations, vehicle location systems typically integrate an absolute positioning system, e.g. GPS, and relative positioning system, e.g. dead-reckoning, with information from maps, making use of the fact that the vehicle is travelling on the road network. This paper describes the structures of a new map-matching algorithm designed to support the navigational function of a real-time vehicle navigation applications. A newly developed sequential update procedure is used together with the outputs of an extended Kalman filter (EKF) formulation for the integration of GPS, Micro-Electro Mechanical System (MEMS) IMU sensor, and a spatial digital database of the road network, to provide continuous, accurate and reliable vehicle location on a given road segment. This is irrespective of the constraints imposed by the operational environment, thus alleviating GPS signal outage and accuracy problems associated with the use of stand-alone location sensors. The system uses the geometrical and topological information of the navigated areas to develop a geographical information system (GIS) with navigation relevant attributes. Based on this model, a logical threshold forcing the vehicle to be located on the boundaries of the streets network is generating the map matching solution. The map matching algorithm provides its solution based on a number of factors such as proximity of the navigation position from the street segment, direction of motion, and the maneuvers of the vehicle. The MEMS IMUs raw data is used as an aid for taking reliable decisions about the maneuvers whenever the geospatial database indicates the possibility of direction change. The GPS/INS/GIS integrated system uses an Extended Kalman Filter (EKF) to estimate the navigation states and to expedite the convergence of the estimated errors. The EKF is updated every computational epoch by two sets of independent measurements; namely the GPS positions and the map matched positions. These sequential updates provide a valuable feedback for the navigation solution to use the map matching results to overcome the degradation in GPS availability and accuracy. The proposed system was tested in downtown Calgary by mounting the navigation system on a land vehicle. The assessment of the developed integrated navigation system is based on comparing the developed navigation solution with a reference trajectory, obtained with higher grade sensors, through which the proposed system provides a significant enhancement in the positional accuracy.
Published in:	Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011) September 20 - 23, 2011 Oregon Convention Center, Portland, Oregon Portland, OR
Pages:	3539 - 3545
Cite this article:	Attia, M., Moussa, A., El-Sheimy, N., "Updating Integrated GPS/INS Systems with Map Matching for Car Navigation Applications," Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 3539-3545.
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