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

^*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語	English
ホスト出版物のタイトル	14th Mediterranean Conference on Control and Automation, MED'06
DOI	https://doi.org/10.1109/MED.2006.328798
出版ステータス	Published - 2006
外部発表	はい
イベント	14th Mediterranean Conference on Control and Automation, MED'06 - Ancona 継続期間: 2006 6月 28 → 2006 6月 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

コンピュータサイエンスの応用
制御およびシステム工学
電子工学および電気工学

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10.1109/MED.2006.328798

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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",

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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

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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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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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