Abstract
This paper describes a Takagi-Sugeno (T-S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association (JPDA) method.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Symposium on Industrial Electronics |
| Pages | 244-249 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009 - Seoul, Korea, Republic of Duration: Jul 5 2009 → Jul 8 2009 |
Other
| Other | IEEE International Symposium on Industrial Electronics, IEEE ISIE 2009 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 7/5/09 → 7/8/09 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
Cite this
T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots. / Watanabe, Keigo; Pathiranage, Chandima Dedduwa; Izumi, Kiyoaka.
IEEE International Symposium on Industrial Electronics. 2009. p. 244-249 5217924.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots
AU - Watanabe, Keigo
AU - Pathiranage, Chandima Dedduwa
AU - Izumi, Kiyoaka
PY - 2009
Y1 - 2009
N2 - This paper describes a Takagi-Sugeno (T-S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association (JPDA) method.
AB - This paper describes a Takagi-Sugeno (T-S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association (JPDA) method.
UR - http://www.scopus.com/inward/record.url?scp=77950101235&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950101235&partnerID=8YFLogxK
U2 - 10.1109/ISIE.2009.5217924
DO - 10.1109/ISIE.2009.5217924
M3 - Conference contribution
AN - SCOPUS:77950101235
SN - 9781424443499
SP - 244
EP - 249
BT - IEEE International Symposium on Industrial Electronics
ER -