Fusing Depth and Video Using Rao-Blackwellized Particle Filter

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 Motion (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 magnitude 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 which is an im...

• Type: Conference
• Year: 2005
• Where: PREMI
• Authors: Amit K. Agrawal, Rama Chellappa
• Keywords: Low Depth Variability, Noisy Depth Data, Particle filter, PREMI 2005