Fusion of Map and Sensor Data in a Modern Car Navigation System |
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Authors: | Dragan Obradovic Henning Lenz and Markus Schupfner |
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Affiliation: | (1) Siemens AG, Corporate Technology, Information and Communications, CT IC4, Otto-Hahn-Ring 6, 81730 Munich, Germany;(2) SiemensVDO, Interior and Infotainment, Infotainment Solutions, Im Gewerbepark C 27, 93059 Regensburg, Germany |
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Abstract: | The main tasks of car navigation systems are positioning, routing, and guidance. This paper describes a novel, two-step approach
to vehicle positioning founded on the appropriate combination of the in-car sensors, GPS signals, and a digital map. The first
step is based on the application of a Kalman filter, which optimally updates the model of car movement based on the in-car
odometer and gyroscope measurements, and the GPS signal. The second step further improves the position estimate by dynamically
comparing the continuous vehicle trajectory obtained in the first step with the candidate trajectories on a digital map. This
is in contrast with standard applications of the digital map where the current position estimate is simply projected on the
digital map at every sampling instant.
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Keywords: | GPS signals vehicle positioning Kalman filter pattern matching map matching dead reckoning |
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