Abstract
This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using pseudolinear process and measurement models. Takagi-Sugeno (T-S) fuzzy model based on observation for nonlinear system is adopted to represent the process and measurement models of the vehicle-landmarks system. Using the Kalman filter theory, each local T-S model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. The simulation results prove that the new approach results in more anticipated performances, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.
| Original language | English |
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| Title of host publication | Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 |
| Pages | 767-770 |
| Number of pages | 4 |
| Publication status | Published - Dec 1 2008 |
| Event | 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 - Oita, Japan Duration: Jan 31 2008 → Feb 2 2008 |
Publication series
| Name | Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 |
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Other
| Other | 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 |
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| Country | Japan |
| City | Oita |
| Period | 1/31/08 → 2/2/08 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
Cite this
Pathiranage, C. D., Watanabe, K., & Izumi, K. (2008). A fuzzy logic based approach to the SLAM problem using pseudolinear models with multiframe data association. In Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08 (pp. 767-770). (Proceedings of the 13th International Symposium on Artificial Life and Robotics, AROB 13th'08).