3D Object Global Localization Using Particle Filter with Back Projection-based Sampling on Saliency

Abstract

Estimating the 3D pose of a target object using particle filter has an important problem of high dimensional search space. Because the objects probably appear anywhere in the search space along the camera ray in the 3D world space, a huge number of samples covering the whole search space are required, which necessitates costly expensive computation time and iterations until convergence. For this reason, we propose a particle filter base on back projection sampling on saliency technique. We obtain the object boundaries as foreground regions using saliency segmentation based on color and depth information that is robust to complex environments. Moreover, we apply the particle filter with sampling, which is based on the back projection technique, using the concept of a relationship between 3D world space and the 2D image plane. The sampling dimension of whole samples along the camera ray can be omitted by generating the samples in the 2D image plane on saliencies before they are back projected into 3D world space using depth information. The required number of samples and iterations are drastically decreased. In addition, our method can perceive the salient regions that may be the region of the target object. Most of the samples will be predicted into these promising regions that make the algorithm converges rapidly.