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DREAM Seminar: Sensor fusion in dynamical systems - applications and research challenges

Seminar: DREAM | December 11 | 4:10-5 p.m. | Soda Hall, Wozniak Lounge


Thomas Schön, Linköping University, Sweden

Electrical Engineering and Computer Sciences (EECS)


Abstract:
Sensor fusion refers to the problem of computing state estimates using measurements from several different, often complementary, sensors. The strategy is explained and (perhaps more importantly) illustrated using four different industrial/research applications, very briefly introduced below. Guided partly by these applications we will highlight key directions for future research within the area of sensor fusion. Given that the number of available sensors is skyrocketing this technology is likely to become even more important in the future. The four applications are; 1. Real-time pose estimation and autonomous landing of the helicopter (using inertial sensors and a camera). 2. Pose estimation of a helicopter using an already existing map (a processed version of an aerial photograph of the operational area), inertial sensors and a camera. 3. Vehicle motion and road surface estimation (using inertial sensors, steering wheel sensor and an infrared camera). 4. Indoor pose estimation of a human body (using inertial sensors and ultra-wideband).

Bio:
Thomas B. Schön is an Associate Professor with the Division of Automatic Control at Linköping University (Linköping, Sweden). He received the BSc degree in Business Administration and Economics in Jan. 2001, the MSc degree in Applied Physics and Electrical Engineering in Sep. 2001 and the PhD degree in Automatic Control in Feb. 2006, all from Linköping University. He has held visiting positions with the University of Cambridge (UK) and the University of Newcastle (Australia). He is a Senior member of the IEEE. He received the best teacher award at the Institute of Technology, Linköping University in 2009. Schön's main research interest is nonlinear inference problems, especially within the context of dynamical systems, solved using probabilistic methods. He is active within the fields of machine learning, signal processing and automatic control. He pursue both basic research and applied research, where the latter is typically carried out in collaboration with industry. More information about his research can be found from his home page: users.isy.liu.se/rt/schon


broman@eecs.berkeley.edu, 510-460-0280