Citation
Thomas B. Schön. "Sensor fusion in dynamical systems - applications and research challenges". Talk or presentation, 11, December, 2012.

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).

Electronic downloads

• dreams-schon-dec-2012.pdf · application/download · 15700 kbytes

• HTML

  Thomas B. Schön. <a
  href="http://chess.eecs.berkeley.edu/pubs/955.html"
  ><i>Sensor fusion in dynamical systems -
  applications and research challenges</i></a>,
  Talk or presentation,  11, December, 2012.

• Plain text

  Thomas B. Schön. "Sensor fusion in
  dynamical systems - applications and research
  challenges". Talk or presentation,  11, December, 2012.

• BibTeX

  @presentation{Schn12_SensorFusionInDynamicalSystemsApplicationsResearchChallenges,
      author = {Thomas B. Schön},
      title = {Sensor fusion in dynamical systems - applications
                and research challenges},
      day = {11},
      month = {December},
      year = {2012},
      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). },
      URL = {http://chess.eecs.berkeley.edu/pubs/955.html}
  }
  

Posted by David Broman on 12 Dec 2012.
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