A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model

Chandima Dedduwa Pathiranage, Keigo Watanabe, Kiyotaka Izumi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes a fuzzy logic based solution to the SLAM problem. Less error prone vehicle process model is proposed to improve the accuracy and the faster convergence of state estimation. Evolution of vehicle motion is modeled using dead-reckoned odometry measurements as control inputs. Nonlinear process model and observation model are formulated as pseudolinear models and approximated by local linear models according to the T-S fuzzy model. Linear Kalman filter equations are then used to estimate the state of the approximated local linear models. Combination of these local state estimates results in global state estimate. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem. The proposed method shows a way to use nonlinear systems in Kalman filter estimator without using Jacobian matrices. It is found that a fuzzy logic based approach with the pseudolinear models provides a demanding solution to state estimation.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages2364-2371
Number of pages8
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: Sep 17 2007Sep 20 2007

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period9/17/079/20/07

Fingerprint

Kalman filters
State estimation
Fuzzy logic
Jacobian matrices
Nonlinear systems

Keywords

  • Kalman filter
  • Pseudolinear model
  • Stability
  • State estimation
  • T-S fuzzy model

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Pathiranage, C. D., Watanabe, K., & Izumi, K. (2007). A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model. In Proceedings of the SICE Annual Conference (pp. 2364-2371). [4421384] https://doi.org/10.1109/SICE.2007.4421384

A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model. / Pathiranage, Chandima Dedduwa; Watanabe, Keigo; Izumi, Kiyotaka.

Proceedings of the SICE Annual Conference. 2007. p. 2364-2371 4421384.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pathiranage, CD, Watanabe, K & Izumi, K 2007, A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model. in Proceedings of the SICE Annual Conference., 4421384, pp. 2364-2371, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, Japan, 9/17/07. https://doi.org/10.1109/SICE.2007.4421384
Pathiranage CD, Watanabe K, Izumi K. A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model. In Proceedings of the SICE Annual Conference. 2007. p. 2364-2371. 4421384 https://doi.org/10.1109/SICE.2007.4421384
Pathiranage, Chandima Dedduwa ; Watanabe, Keigo ; Izumi, Kiyotaka. / A solution to the SLAM problem based on fuzzy Kalman filter using pseudolinear measurement model. Proceedings of the SICE Annual Conference. 2007. pp. 2364-2371
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