@Article{Simanek-AURO2015,
UPDATE   = { 2015-12-09 },
  author =      {{\v S}im{\'a}nek, Jakub and Kubelka, Vladim{\'\i}r and Reinstein, Michal},
  affiliation = {13138-13133-13133},
  authorship =  {34-33-33},
  title =       {Improving multi-modal data fusion by anomaly detection},
  year =        {2015},
  journal =     {Autonomous Robots},
  publisher =   {Springer US},
  address =     {New York City, USA},
  issn =        {0929-5593},
  annote =      {If we aim for autonomous navigation of a mobile
                  robot, it is crucial and essential to have proper
                  state estimation of its position and orientation. We
                  already designed a multi-modal data fusion algorithm
                  that combines visual, laser-based, inertial, and
                  odometric modalities in order to achieve robust
                  solution to a general localization problem in
                  challenging Urban Search and Rescue
                  environment. Since different sensory modalities are
                  prone to different nature of errors, and their
                  reliability varies vastly as the environment changes
                  dynamically, we investigated further means of
                  improving the localization. The common practice
                  related to the EKF-based solutions such as ours is a
                  standard statistical test of the observations or of
                  its corresponding filter residuals performed to
                  reject anomalous data that deteriorate the filter
                  performance. In this paper we show how important it
                  is to treat well visual and laser anomalous
                  residuals, especially in multi-modal data fusion
                  systems where the frequency of incoming observations
                  varies significantly across the modalities. In
                  practice, the most complicated part is to correctly
                  identify the actual anomalies, which are to be
                  rejected, and therefore here lies our major
                  contribution. We go beyond the standard statistical
                  tests by exploring different state-of-the-art
                  machine learning approaches and exploiting our rich
                  dataset that we share with the robotics
                  community. We demonstrate the implications of our
                  research both indoor (with precise reference from a
                  Vicon system) as well as in challenging outdoor
                  environment. In the final, we prove that monitoring
                  the health of the observations in Kalman filtering
                  is something, that is often overlooked, however, it
                  definitively should not be.},
  keywords =    {localization, kalman filter, multi-modal data fusion,
                  anomaly detection, mobile robots},
  note =        {early access},
  project =     {SGS13/144/OHK3/2T/13, GACR 14-13876S, FP7-ICT-609763 TRADR},
  doi =         {10.1007/s10514-015-9431-6},
IS          = { zkontrolovano 22 Dec 2015 },
month       = { August },
pages       = { 139-154 },
volume      = { 39 },
number      = { 2 },
ut_isi      = { 000357652400002 },
scopus      = { 2-s2.0-84921846758 },
}