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

Author

Kurt, Zeyneb ; Yavuz, Szrma

Author_Institution

Bilgisayar Muhendisligi Bolumu, Yildiz Teknik Univ., Istanbul

fYear

2008

fDate

20-22 April 2008

Firstpage

1

Lastpage

4

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 sensorspsila 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.

Keywords

Monte Carlo methods; SLAM (robots); image sensors; mobile robots; sequential estimation; SLAM problem; infrared sensors; particle filter; robot sensor; sequential Monte Carlo method; simultaneous localization and map building algorithm; statistical estimation algorithm; Infrared sensors; Kalman filters; Monte Carlo methods; Particle filters; Radio frequency; Robot sensing systems; Simultaneous localization and mapping; Sliding mode control; Sonar; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, Communication and Applications Conference, 2008. SIU 2008. IEEE 16th

Conference_Location

Aydin

Print_ISBN

978-1-4244-1998-2

Electronic_ISBN

978-1-4244-1999-9

Type

conf

DOI

10.1109/SIU.2008.4632712

Filename

4632712