@Article{Simanek-MECH2014,
  IS = { zkontrolovano 29 May 2014 },
  UPDATE  = { 2014-05-29 },
  author =      {{\v S}im{\'a}nek, Jakub and Reinstein, Michal and Kubelka, Vladim{\'\i}r},
  affiliation = {13138-13133-13133},
  authorship =  {34-33-33},
  title =       {Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots},
  year =        {2014},
  month =       {},
  pages =       {1-6},
  journal =     {IEEE-ASME Transactions on Mechatronics},
  publisher =   {IEEE and ASME},
  address =     {3 and 2 Park Avenue, New York, NY, USA},
  issn =        {1083-4435},
  volume =      {},
  number =      {99},
  annote =      {This paper presents evaluation of 4 different state
                  estimation architectures exploiting the extended
                  Kalman filter (EKF) for 6DOF dead reckoning of a
                  mobile robot. The EKF is a well proven and commonly
                  used technique for fusion of inertial data and
                  robot's odometry. However, different approaches to
                  designing the architecture of the state estimator
                  lead to different performance and computational
                  demands. While seeking the best possible solution
                  for the mobile robot, the nonlinear model and the
                  error model are addressed, both with and without a
                  complementary filter for attitude estimation. The
                  performance is determined experimentally by means of
                  precision of both indoor and outdoor navigation,
                  including complex structured environment such as
                  stairs and rough terrain. According to the
                  evaluation, the nonlinear model combined with the
                  complementary filter is selected as a best candidate
                  (reaching 0.8m RMSE and average of 4{\%} return
                  position error of distance driven) and implemented
                  for real-time onboard processing during a rescue
                  mission deployment.},
  keywords =    {urban search and rescue, complementary filter, extended Kalman filter},
  note =        {early access},
  project =     {FP7-ICT-247870, FP7-ICT-609763},
  doi =         {10.1109/TMECH.2014.2311416},
  ut_isi =      {},
  scopus =      {},
}