Abstract
This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on an observation for a nonlinear system is adopted to represent the process and measurement models of the vehicle-landmark system. The complete system of the vehiclelandmark model is decomposed into several linear models. Using the Kalman filter theory, each local 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 shows that the new approach performs better, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.
| Original language | English |
|---|---|
| Title of host publication | IFAC Proceedings Volumes (IFAC-PapersOnline) |
| Volume | 17 |
| Edition | 1 PART 1 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of Duration: Jul 6 2008 → Jul 11 2008 |
Other
| Other | 17th World Congress, International Federation of Automatic Control, IFAC |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 7/6/08 → 7/11/08 |
Fingerprint
Keywords
- Fuzzy and neural systems relevant to control and identification
- Robust fuzzy control
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
A fuzzy Kalman filter approach to the SLAM problem of nonholonomic mobile robots. / Watanabe, Keigo; Dedduwa Pathiranage, Chandima; Izumi, Kiyotaka.
IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 17 1 PART 1. ed. 2008.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A fuzzy Kalman filter approach to the SLAM problem of nonholonomic mobile robots
AU - Watanabe, Keigo
AU - Dedduwa Pathiranage, Chandima
AU - Izumi, Kiyotaka
PY - 2008
Y1 - 2008
N2 - This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on an observation for a nonlinear system is adopted to represent the process and measurement models of the vehicle-landmark system. The complete system of the vehiclelandmark model is decomposed into several linear models. Using the Kalman filter theory, each local 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 shows that the new approach performs better, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.
AB - This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on an observation for a nonlinear system is adopted to represent the process and measurement models of the vehicle-landmark system. The complete system of the vehiclelandmark model is decomposed into several linear models. Using the Kalman filter theory, each local 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 shows that the new approach performs better, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.
KW - Fuzzy and neural systems relevant to control and identification
KW - Robust fuzzy control
UR - http://www.scopus.com/inward/record.url?scp=79961018380&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79961018380&partnerID=8YFLogxK
U2 - 10.3182/20080706-5-KR-1001.0942
DO - 10.3182/20080706-5-KR-1001.0942
M3 - Conference contribution
AN - SCOPUS:79961018380
SN - 9783902661005
VL - 17
BT - IFAC Proceedings Volumes (IFAC-PapersOnline)
ER -