Sensor fusion for positioning of an autonomous vehicle

Abstract: The automotive industry has currently a high focus on automating road vehicles. Positive environmental impact can be achieved if carsharing becomes more common, aiding fewer cars on the roads. When the human factor in driving decreases, positive effects may be seen in traffic safety. But many challanges remain, for example the questions of liability. The vehicles must be able to detect their surroundings and the sensors need redundancy. Sensor fusion techniques increase the reliability of measurement results by combining measurement results from multiple different sensors. This thesis uses inertial sensors to calculate position and heading. An unscented Kalman filter has been designed and implemented on a demonstrator. The demonstrator consists of an r/c car with autonomous functions. It has a forward-facing camera and it can follow road sidelines. The Kalman filter incorporates measurements from two incremental encoders, a gyroscope and a steering angle sensor. The result shows that the combination of sensor measurements provides a better estimation of position and direction of travel.