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Reconstruction of Rigid Body Models from Motion Distorted Laser Range Data Using Optical Flow
Ilg, E. and K├╝mmerle, R. and Burgard, W. and Brox, T.
Proc. of the IEEE Int.~Conf.~on Robotics and Automation (ICRA)
2014
ilg14icra.pdf



BibTeX:
@inproceedings{ilg14icra,
  author         = {Ilg, E. and K{\"u}mmerle, R. and Burgard, W. and
                   Brox, T.},
  title          = {Reconstruction of Rigid Body Models from Motion
                   Distorted Laser Range Data Using Optical Flow},
  booktitle      = {Proc.~of the IEEE Int.~Conf.~on Robotics and
                   Automation (ICRA)},
  year           = 2014,
  url            = {http://ais.informatik.uni-freiburg.de/publications/papers/ilg14icra.pdf},
  address        = {Hong Kong, China},
  abstract       = {The setup of tilting a 2D laser range finder up
                   and down is a widespread strategy to acquire 3D
                   point clouds.  This setup requires that the scene
                   is static while the robot takes a 3D scan.  If an
                   object moves through the scene during the
                   measurement process and one does not take into
                   account these movements, the resulting model will
                   get distorted.  This paper presents an approach to
                   reconstruct the 3D model of a moving rigid object
                   from the inconsistent set of 2D measurements by the
                   help of a camera.  Our approach utilizes optical
                   flow in the camera images to estimate the motion in
                   the image plane and point-line constraints to
                   compensate the missing information about the motion
                   in depth.  We combine multiple sweeps and/or views
                   into to a single consistent model using a point-to-
                   plane ICP approach and optimize single sweeps by
                   smoothing the resulting trajectory.  Experiments
                   obtained in real outdoor scenarios with moving cars
                   demonstrate that our approach yields accurate
                   models.},
}