DocumentCode
2650846
Title
An SLAM algorithm based on improved UKF
Author
Qu, Liping ; He, Shuiqing ; Qu, Yongyin
Author_Institution
Dept. of Electr. Inf. Eng., Coll. Univ. of Beihua, Jilin, China
fYear
2012
fDate
23-25 May 2012
Firstpage
4154
Lastpage
4157
Abstract
Because of using system nonlinear model directly UKF overcomes the shortcomings of the methods such as EKF that they easily introduces truncation errors in the process of lining model .So it is widely used in SLAM problem. Because the square root of filter has the advantages that it can ensure the covariance matrix nonnegative, a square root version of the UKF was included in the SLAM problem that improve the performance of UKF-SLAM algorithm. Simulation result shows that this algorithm is effective.
Keywords
Kalman filters; SLAM (robots); covariance matrices; mobile robots; nonlinear filters; covariance matrix; filter square root; improved UKF-based SLAM algorithm; lining model process; mobile robot; nonlinear model; square root version; truncation errors; Covariance matrix; Filtering algorithms; Kalman filters; Mathematical model; Simultaneous localization and mapping; Mobile Robot; SLAM; Unscented Kalman filter;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location
Taiyuan
Print_ISBN
978-1-4577-2073-4
Type
conf
DOI
10.1109/CCDC.2012.6243112
Filename
6243112
Link To Document