A simultaneous localization and map building algorithm based on sequential Monte Carlo method

Zeyneb Kurt; Sirma Yavuz

doi:10.1109/siu.2008.4632712

A simultaneous localization and map building algorithm based on sequential Monte Carlo method

Zeyneb Kurt, Sirma Yavuz

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

1 Citation (Scopus)

Abstract

In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems in SLAM algorithms are sensors' noise and odometry errors. To solve this problem, sequential Monte Carlo (SMC) method which is a well known particle filter application is used and promising results were obtained for the SLAM problem.

Original language	English
Title of host publication	2008 IEEE 16th Signal Processing, Communication and Applications Conference
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Print)	9781424419982
DOIs	https://doi.org/10.1109/siu.2008.4632712
Publication status	Published - 20 Apr 2008
Externally published	Yes
Event	2008 IEEE 16th Signal Processing, Communication and Applications Conference - Aydin, Turkey Duration: 20 Apr 2008 → 22 Apr 2008

Conference

Conference	2008 IEEE 16th Signal Processing, Communication and Applications Conference
Country/Territory	Turkey
City	Aydin
Period	20/04/08 → 22/04/08

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10.1109/siu.2008.4632712

Cite this

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title = "A simultaneous localization and map building algorithm based on sequential Monte Carlo method",

abstract = "In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems in SLAM algorithms are sensors' noise and odometry errors. To solve this problem, sequential Monte Carlo (SMC) method which is a well known particle filter application is used and promising results were obtained for the SLAM problem.",

author = "Zeyneb Kurt and Sirma Yavuz",

year = "2008",

month = apr,

day = "20",

doi = "10.1109/siu.2008.4632712",

language = "English",

isbn = "9781424419982",

booktitle = "2008 IEEE 16th Signal Processing, Communication and Applications Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2008 IEEE 16th Signal Processing, Communication and Applications Conference ; Conference date: 20-04-2008 Through 22-04-2008",

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Kurt, Z & Yavuz, S 2008, A simultaneous localization and map building algorithm based on sequential Monte Carlo method. in 2008 IEEE 16th Signal Processing, Communication and Applications Conference., 4632712, Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ, 2008 IEEE 16th Signal Processing, Communication and Applications Conference, Aydin, Turkey, 20/04/08. https://doi.org/10.1109/siu.2008.4632712

A simultaneous localization and map building algorithm based on sequential Monte Carlo method. / Kurt, Zeyneb; Yavuz, Sirma.
2008 IEEE 16th Signal Processing, Communication and Applications Conference. Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc., 2008. 4632712.

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

TY - GEN

T1 - A simultaneous localization and map building algorithm based on sequential Monte Carlo method

AU - Kurt, Zeyneb

AU - Yavuz, Sirma

PY - 2008/4/20

Y1 - 2008/4/20

N2 - In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems in SLAM algorithms are sensors' noise and odometry errors. To solve this problem, sequential Monte Carlo (SMC) method which is a well known particle filter application is used and promising results were obtained for the SLAM problem.

AB - In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems in SLAM algorithms are sensors' noise and odometry errors. To solve this problem, sequential Monte Carlo (SMC) method which is a well known particle filter application is used and promising results were obtained for the SLAM problem.

U2 - 10.1109/siu.2008.4632712

DO - 10.1109/siu.2008.4632712

M3 - Conference contribution

SN - 9781424419982

BT - 2008 IEEE 16th Signal Processing, Communication and Applications Conference

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway, NJ

T2 - 2008 IEEE 16th Signal Processing, Communication and Applications Conference

Y2 - 20 April 2008 through 22 April 2008

ER -

A simultaneous localization and map building algorithm based on sequential Monte Carlo method

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