• DocumentCode
    2133422
  • Title

    A sensor-centric EKF for inertial-aided visual odometry

  • Author

    Kleinert, Moritz ; Stilla, Uwe

  • Author_Institution
    Fraunhofer IOSB, Ettlingen, Germany
  • fYear
    2013
  • fDate
    28-31 Oct. 2013
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    When appropriate infrastructure is not available, localization of pedestrians becomes a difficult task. This is especially the case in urban or indoor scenarios, where satellite navigation is hindered due to occlusions or multipath effects. A promising alternative is to combine a small, low-cost inertial measurement unit (IMU) with a camera in order to exploit the complementary error characteristics of these devices by simultaneously estimating the positions of observed landmarks and the trajectory of the sensor system with a stochastic filter.
  • Keywords
    Kalman filters; computational complexity; distance measurement; image sensors; inertial navigation; nonlinear filters; pedestrians; EKF; IMU; SLAM; camera; computational complexity; extended Kalman filter; indoor scenarios; inertial-aided visual odometry; jacobians; low-cost inertial measurement unit; monocular simultaneous localization and mapping; multipath effects; observed landmarks; occlusions; pedestrians localization; satellite navigation; sensor system trajectory; sensor-centric EKF; sensor-centric formulation; state prediction equations; stochastic filter; urban scenarios; Cameras; Covariance matrices; Equations; Mathematical model; Navigation; Simultaneous localization and mapping; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on
  • Conference_Location
    Montbeliard-Belfort
  • Type

    conf

  • DOI
    10.1109/IPIN.2013.6817915
  • Filename
    6817915