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 - Jan 1 2010 |
| Externally published | Yes |
Keywords
- Data association
- Fuzzy Kalman filtering
- Pseudolinear model
- Simultaneous localization and mapping
- T-S fuzzy model
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Geometry and Topology