Paper Info
Title
Environment Understanding: Robust Feature Extraction from Range Sensor Data
Abstract
This paper proposes an approach allowing indoor environment supervised learning to recognize relevant features for environment understanding. Stochastic preprocessing meth- ods 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 geo- metrical interpretation of their main components, and analyzing their robustness to shape distortions and scale changes.
Year
DOI
Venue
2006
10.1109/IROS.2006.282509
Beijing
Keywords
Field
DocType
feature extraction,learning (artificial intelligence),mobile robots,principal component analysis,stochastic processes,Fisher linear discriminant analysis,indoor environment supervised learning,pattern recognition,principal components analysis,range sensor data,robust feature extraction,scale changes,shape distortions,stochastic preprocessing methods
Computer vision,Pattern recognition,Computer science,Stochastic process,Supervised learning,Feature extraction,Robustness (computer science),Preprocessor,Artificial intelligence,Linear discriminant analysis,Mobile robot,Principal component analysis
Conference
ISBN
Citations 
PageRank 
1-4244-0259-X
2
0.42
References 
Authors
14
2
Name
Order
Citations
PageRank
Antonio Romeo120.42
Luis Montano2545.24