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

Daniele Caltabiano; Giovanni Muscato; Salvatore Sessa

doi:10.1109/MED.2006.328798

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

Daniele Caltabiano^*, Giovanni Muscato, Salvatore Sessa

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	14th Mediterranean Conference on Control and Automation, MED'06
DOIs	https://doi.org/10.1109/MED.2006.328798
Publication status	Published - 2006
Externally published	Yes
Event	14th Mediterranean Conference on Control and Automation, MED'06 - Ancona Duration: 2006 Jun 28 → 2006 Jun 30

Other

Other	14th Mediterranean Conference on Control and Automation, MED'06
City	Ancona
Period	06/6/28 → 06/6/30

ASJC Scopus subject areas

Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/MED.2006.328798

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@inproceedings{00b2c75ff8d0491baad087155ef58fa2,

title = "A new global localization algorithm based on feature extraction and particle filter",

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.",

author = "Daniele Caltabiano and Giovanni Muscato and Salvatore Sessa",

year = "2006",

doi = "10.1109/MED.2006.328798",

language = "English",

isbn = "0978672003",

booktitle = "14th Mediterranean Conference on Control and Automation, MED'06",

note = "14th Mediterranean Conference on Control and Automation, MED'06 ; Conference date: 28-06-2006 Through 30-06-2006",

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AU - Muscato, Giovanni

AU - Sessa, Salvatore

PY - 2006

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N2 - 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.

AB - 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.

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DO - 10.1109/MED.2006.328798

M3 - Conference contribution

AN - SCOPUS:35948950335

SN - 0978672003

SN - 9780978672003

BT - 14th Mediterranean Conference on Control and Automation, MED'06

T2 - 14th Mediterranean Conference on Control and Automation, MED'06

Y2 - 28 June 2006 through 30 June 2006

ER -

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

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