Motion State Estimation for an Autonomous Vehicle- Trailer System Using Kalman Filtering-based Multisensor Data Fusion
نویسنده
چکیده
In this research we present a Kalman filtering-based motion state estimation method for an autonomous vehicle-trailer system by fusing multiple sensor data, which can be applied directly to autonomous navigation and motion control. The autonomous vehicle-trailer system consists of an autonomous vehicle and a passive trailer which are coupled by a trailer hitch. Our vehicle-trailer system is equipped with the Global Positioning System (GPS), endocder-based odometry, and hitch angle sesnors. Using GPS and odometry sensors, we obtain two independent vehicle motion information which includes orientation, position (localization), and velocity data. We then apply a Kalman filtering method by fusing odometry, GPS, and hitch angle sesnsor data simultaneously to provide robust and more accurate motion state estimation for the vehicel-trailer system in the presence of sensor noises. Finally, we verify our Kalman filtering-based motion state estimation method in simulation and experimental tests.
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