By Topic

Environment Understanding: Robust Feature Extraction from Range Sensor Data

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
$31 $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)
Romeo, A. ; Dept. de Inf. e Ingenieria de Sistemas, Univ. de Zaragoza ; Montano, L.

This paper proposes an approach allowing indoor environment supervised learning to recognize relevant features for environment understanding. Stochastic preprocessing methods in combination with either of usual pattern recognition schemes are used. Preprocessing method treated is a combination of the principal components analysis and the Fisher linear discriminant analysis well adapted to the sensorial information and to the kind of environments considered. The supervised method is applied to the raw range data obtained from typical indoor environments, obtaining good recognition performances without geometrical feature extraction, allowing its real time implementation. Our work focuses on the preprocessing method, giving a geometrical interpretation of their main components, and analyzing their robustness to shape distortions and scale changes

Published in:

Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on

Date of Conference:

9-15 Oct. 2006