• DocumentCode
    880161
  • Title

    Inverse Depth Parametrization for Monocular SLAM

  • Author

    Civera, Javier ; Davison, Andrew J. ; Montiel, J. M Martínez

  • Volume
    24
  • Issue
    5
  • fYear
    2008
  • Firstpage
    932
  • Lastpage
    945
  • Abstract
    We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features relative to the camera locations from which they were first viewed, which produces measurement equations with a high degree of linearity. Importantly, our parametrization can cope with features over a huge range of depths, even those that are so far from the camera that they present little parallax during motion---maintaining sufficient representative uncertainty that these points retain the opportunity to "come in\´\´ smoothly from infinity if the camera makes larger movements. Feature initialization is undelayed in the sense that even distant features are immediately used to improve camera motion estimates, acting initially as bearing references but not permanently labeled as such. The inverse depth parametrization remains well behaved for features at all stages of SLAM processing, but has the drawback in computational terms that each point is represented by a 6-D state vector as opposed to the standard three of a Euclidean XYZ representation. We show that once the depth estimate of a feature is sufficiently accurate, its representation can safely be converted to the Euclidean XYZ form, and propose a linearity index that allows automatic detection and conversion to maintain maximum efficiency---only low parallax features need be maintained in inverse depth form for long periods. We present a real-time implementation at 30 Hz, where the parametrization is validated in a fully automatic 3-D SLAM system featuring a handheld single camera with no additional sensing. Experiments show robust operation in challenging indoor and outdoor environments with a very large ranges of scene depth, varied motion, and als- - o real time 360deg loop closing.
  • Keywords
    Kalman filters; SLAM (robots); image sensors; motion estimation; nonlinear filters; camera motion estimates; extended Kalman filter; feature initialization; inverse depth parametrization; monocular SLAM; monocular simultaneous localization and mapping; Monocular simultaneous localization and mapping (SLAM); real-time vision;
  • fLanguage
    English
  • Journal_Title
    Robotics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1552-3098
  • Type

    jour

  • DOI
    10.1109/TRO.2008.2003276
  • Filename
    4637878