Sensor fusion förpositionberäkning av autonoma fordon

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. 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.Fordonsindustrin har högt fokus på att automatisera vägfordon. Positiv miljöpåverkan kan uppstå om bildelning blir vanligare och om det blir mindre bilar på vägarna. När den mänskliga faktorn minskar så tillåts högre trafiksäkerhet. Men många tekniska utmaningar kvarstår, speciellt kring ansvarfrågor. Fordonen måste kunna känna av sin omgivning och sensorerna behöver redundans. Sensor fusion tekniker ökar pålitligheten av mätresultat genom att kombinera mätresultat från flera olika sensorer. Detta examensarbete använder tröghetssensorer för att beräkna position och körriktning. Ett unscented Kalman filter har designats och implementerats på en demonstrator. Demonstratorn består av en radiostyrd bil med autonoma funktioner. Den har en framåt-riktad kamera och kan följa väglinjer. Kalman filtret tar in mätresultat från två vinkelgivare, ett gyroskåp och en styrvinkel sensor. Resultatet visar att kombinationen av sensorerna ger en bättre estimering av position och körriktning