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Sensor-data-fusion for an autonomous vehicle using a Kalman-filter

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2 Author(s)
Kasper, R. ; Otto-von-Guericke Univ. Magdeburg, Magdeburg ; Schmidt, S.

This paper presents a method to estimate the system-state, especially the full position, of an autonomous vehicle using sensor data fusion of redundant position signals based on an extended Kalman-filter. The position is detected with the help of magnet sensors attached at the vehicle and a global camera signal with low resolution, similar to GPS. A lane marked with permanent magnets and an infrared camera are used for this purpose. The vehiclepsilas driving dynamics are described using a nonlinear single-track model.

Published in:

Intelligent Systems and Informatics, 2008. SISY 2008. 6th International Symposium on

Date of Conference:

26-27 Sept. 2008