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. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T-S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method 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 method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter - SLAM algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 345-364 |
| Number of pages | 20 |
| Journal | Soft Computing |
| Volume | 14 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |
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Keywords
- Data association
- Fuzzy Kalman filtering
- Pseudolinear model
- Simultaneous localization and mapping
- T-S fuzzy model
ASJC Scopus subject areas
- Software
- Geometry and Topology
- Theoretical Computer Science
Cite this
T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots. / Pathiranage, Chandima Dedduwa; Watanabe, Keigo; Izumi, Kiyotaka.
In: Soft Computing, Vol. 14, No. 4, 2010, p. 345-364.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - T-S fuzzy model adopted SLAM algorithm with linear programming based data association for mobile robots
AU - Pathiranage, Chandima Dedduwa
AU - Watanabe, Keigo
AU - Izumi, Kiyotaka
PY - 2010
Y1 - 2010
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. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T-S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method 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 method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter - SLAM algorithm.
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. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T-S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method 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 method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter - SLAM algorithm.
KW - Data association
KW - Fuzzy Kalman filtering
KW - Pseudolinear model
KW - Simultaneous localization and mapping
KW - T-S fuzzy model
UR - http://www.scopus.com/inward/record.url?scp=70350557380&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350557380&partnerID=8YFLogxK
U2 - 10.1007/s00500-009-0409-1
DO - 10.1007/s00500-009-0409-1
M3 - Article
AN - SCOPUS:70350557380
VL - 14
SP - 345
EP - 364
JO - Soft Computing
JF - Soft Computing
SN - 1432-7643
IS - 4
ER -