@InProceedings{reinstein-icra2013,
  IS = { zkontrolovano 24 Jan 2014 },
  UPDATE   = { 2014-01-06 },
  author =      {Reinstein, Michal and Kubelka, Vladimir and Zimmermann, Karel},
  title =       {Terrain Adaptive Odometry for Mobile Skid-steer Robots},
  year =        {2013},
  pages    = { 4706-4711 },
  booktitle =   {ICRA2013: Proceedings of 2013 IEEE International Conference on Robotics and Automation},
  publisher =   {IEEE},
  address =     {Piscataway, USA},
  isbn     = { 978-1-4673-5641-1 },
  issn = {1050-4729},
  book_pages =  {5865},
  editor      = { Parker, Lynne },
  month =       {May},
  day =         {6-10},
  venue =       {Karlsruhe, Germany},
  organization ={IEEE Robotics and Automation Society},
  annote =      {This paper proposes a novel approach to improving
    precision and reliability of odometry of skid-steer mobile robots
    by means inspired by robotic terrain classification (RTC). In
    contrary to standard RTC approaches we do not provide human
    labeled discrete terrain categories but we classify the terrain
    directly by the values of coefficients correcting the robot's
    odometry. Hence these coefficients make the odometry model
    adaptable to the terrain type due to inherent slip
    compensation. Estimation of these correction coefficients is based
    on feature extraction from the vibration data measured by an
    inertial measurement unit and regression function trained
    offline. Statistical features from the time domain, frequency
    domain, and wavelet features were explored and the best were
    automatically selected. To provide ground truth trajectory for the
    purpose of offline training a portable overhead camera tracking
    system was developed. Experimental evaluation on rough outdoor
    terrain proved 67.9 a 7.5% improvement in RMSE in position with
    respect to a state of the art odometry model. Moreover, our
    proposed approach is straightforward, easy for online
    implementation, and low on computational demands.},
  keywords =    {robotic terrain classification, linear regression, 
    odometry, slip compensation},
  prestige =    {important},
  note =        {CD-ROM},
  project =     {FP7-SPACE-241523 PRoViScout, FP7-ICT-247870 NIFTi, GACR P103/11/P700},
doi         = { 10.1109/ICRA.2013.6631247 },
  ut_isi =      {},
  www         = {http://www.icra2013.org/},
}