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Fusing Depth and Video using Rao-Blackwellized Particle Filter
 

Summary: Fusing Depth and Video using
Rao-Blackwellized Particle Filter
Amit Agrawal and Rama Chellappa
University of Maryland
College Park, MD 20742 USA
aagrawal@cfar.umd.edu
Abstract. We address the problem of fusing sparse and noisy depth
data obtained from a range finder with features obtained from intensity
images to estimate ego-motion and refine 3D structure of a scene using
a Rao-Blackwellized particle filter. For scenes with low depth variability,
the algorithm shows an alternate way of performing Structure from Mo-
tion (SfM) starting with a flat depth map. Instead of using 3D depths,
we formulate the problem using 2D image domain parallax and show
that conditioned on non-linear motion parameters, the parallax magni-
tude with respect to the projection of the vanishing point forms a linear
subsystem independent of camera motion and their distributions can be
analytically integrated. Thus, the structure is obtained by estimating
parallax with respect to the given depths using a Kalman filter and only
the ego-motion is estimated using a particle filter. Hence, the required
number of particles becomes independent of the number of feature points

  

Source: Agrawal, Amit - Mitsubishi Electric Research Labs

 

Collections: Engineering