By Topic

An improved geometric Radial Basis function Network for Hand-Eye Calibration

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Eduardo Vázquez-Santacruz ; Department of Electrical Engineering and Computer Sciences, CINVESTAV Guadalajara. Av. del Bosque 1145, El Bajío, Zapopan, 45019, Jalisco, México ; Eduardo Bayro-Corrochano

In this paper we present the application of a new hypercomplex-valued Radial Basis Network (RBF) to estimate unknown geometric transformations such as in the case of the Hand-Eye Calibration problem. This network constitutes a generalization of the standard real-valued RBF. The network fed with geometric entities can be used in real time to estimate changes in the linear transformation between the coordinate system of the camera and the coordinate system of the end-effector. This approach is more efficient than standard batch methods particularly because our method works in real time, estimating the rigid transformation under temporal perturbation. In contrast, the standard methods need to recalibrate each time first by collecting data and then by computing a batch procedure often using SVD or optimization techniques.

Published in:

Neural Networks (IJCNN), The 2011 International Joint Conference on

Date of Conference:

July 31 2011-Aug. 5 2011