A new global localization algorithm based on feature extraction and particle filter

Author

Caltabiano, Daniele ; Muscato, Giovanni ; Sessa, Salvatore

Author_Institution

Univ. degli Studi di Catania

fYear

2006

fDate

28-30 June 2006

Firstpage

1

Lastpage

6

Abstract

This paper describes a new global localization algorithm based on feature extraction and particle filter. This algorithm uses two kinds of sensors: wheels encoders and a laser scanner. A map of the environment is built by taking laser readings of the environment from well-known poses of the robot. The resulting map is composed by a list of features, representing the position of clusters obtained by using the mean shift algorithm. The mean shift algorithm is also applied for each sampling step in order to calculate the importance factor of the particles. The trials have been conducted by using a simulator of a differential drive robot

Keywords

feature extraction; mobile robots; optical scanners; particle filtering (numerical methods); pattern clustering; pose estimation; sensors; cluster position; differential drive robot; feature extraction; global localization algorithm; laser scanner; mean shift algorithm; particle filter; sensors; wheel encoders; Clustering algorithms; Computational modeling; Feature extraction; Iterative algorithms; Mobile robots; Particle filters; Robot localization; Robot sensing systems; Sampling methods; Wheels;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2006. MED '06. 14th Mediterranean Conference on

Conference_Location

Ancona

Print_ISBN

0-9786720-1-1

Electronic_ISBN

0-9786720-0-3

Type

conf

DOI

10.1109/MED.2006.328798

Filename

4124971