TY - GEN
T1 - Feature based estimation for mapping robot environments using fuzzy Kalman filter
AU - Pathiranage, Chandima Dedduwa
AU - Watanabe, Keigo
AU - Izumi, Kiyotaka
PY - 2007/12/1
Y1 - 2007/12/1
N2 - This paper introduces a fuzzy Kalman filter based approach for mapping robot environments. Takagi-Sugeno fuzzy models for nonlinear system are adopted to represent the vehicle and landmarks state equations. The complete system of the vehicle and landmarks 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. This estimator is simulated using Matlab for the vehicle-landmark system and results prove that the new approach can accurately map the environment.
AB - This paper introduces a fuzzy Kalman filter based approach for mapping robot environments. Takagi-Sugeno fuzzy models for nonlinear system are adopted to represent the vehicle and landmarks state equations. The complete system of the vehicle and landmarks 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. This estimator is simulated using Matlab for the vehicle-landmark system and results prove that the new approach can accurately map the environment.
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M3 - Conference contribution
AN - SCOPUS:78549250391
SN - 9784990288013
T3 - Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07
SP - 748
EP - 751
BT - Proceedings of the 12th International Symposium on Artificial Life and Robotics, AROB 12th'07
T2 - 12th International Symposium on Artificial Life and Robotics, AROB 12th'07
Y2 - 25 January 2007 through 27 January 2007
ER -