Target Model Estimation using Particle Filters for Visual Servoing

Author

Hafez, A. H. Abdul ; Jawahar, C.V.

Author_Institution

Dept. of CSE, Osmania Univ., Hyderabad

Volume

4

fYear

0

fDate

0-0 0

Firstpage

651

Lastpage

654

Abstract

In this paper, we present a novel method for model estimation for visual servoing. This method employs a particle filter algorithm to estimate the depth of the image features online. A Gaussian probabilistic model is employed to model the object points in the current camera frame. A set of 3D samples drawn from the model is projected into the image space in the next frame. The 3D sample that maximizes the likelihood is considered to be the most probable real-world 3D point. The variance value of the depth density function converges to very small value within a few iterations. Results show accurate estimate of the depth/model and a high level of stability in the visual servoing process

Keywords

Gaussian processes; end effectors; feature extraction; maximum likelihood estimation; probability; robot vision; 3D sample; Gaussian probabilistic model; depth density function; depth estimation; image features; image space; likelihood maximization; particle filter algorithm; target model estimation; visual servoing; Bayesian methods; Cameras; Density functional theory; Educational institutions; Image converters; Information technology; Jacobian matrices; Particle filters; Stability; Visual servoing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.1103

Filename

1699925