Global Motion Estimation from Relative Measurements in the Presence of Outliers View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Guillaume Bourmaud , Rémi Mégret , Audrey Giremus , Yannick Berthoumieu

ABSTRACT

This work addresses the generic problem of global motion estimation (homographies, camera poses, orientations, etc.) from relative measurements in the presence of outliers. We propose an efficient and robust framework to tackle this problem when motion parameters belong to a Lie group manifold. It exploits the graph structure of the problem as well as the geometry of the manifold. It is based on the recently proposed iterated extended Kalman filter on matrix Lie groups. Our algorithm iteratively samples a minimum spanning tree of the graph, applies Kalman filtering along this spanning tree and updates the graph structure, until convergence. The graph structure update is based on computing loop errors in the graph and applying a proposed statistical inlier test on Lie groups. This is done efficiently, taking advantage of the covariance matrix of the estimation errors produced by the filter. The proposed formalism is applied on both synthetic and real data, for a camera pose registration problem, an automatic image mosaicking problem and a partial 3D reconstruction merging problem. In these applications, the framework presented in this paper efficiently recovers the global motions while the state of the art algorithms fail due to the presence of a large proportion of outliers. More... »

PAGES

366-381

References to SciGraph publications

  • 2013-07. Rotation Averaging in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007-08. Automatic Panoramic Image Stitching using Invariant Features in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2006. Robustness in Motion Averaging in COMPUTER VISION – ACCV 2006
  • 2012. Stochastic Models, Information Theory, and Lie Groups, Volume 2, Analytic Methods and Modern Applications in NONE
  • Book

    TITLE

    Computer Vision -- ACCV 2014

    ISBN

    978-3-319-16813-5
    978-3-319-16814-2

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-16814-2_24

    DOI

    http://dx.doi.org/10.1007/978-3-319-16814-2_24

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1049158738


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