Abstract
This chapter first introduces the concept of SLAM for navigation of mobile robots and then describes the extended Kalman filter (EKF) based SLAM algorithms in detail. Next we consider a more complex scenario where this EKF based SLAM algorithm is implemented in presence of incorrect knowledge of sensor statistics and discuss how fuzzy or neuro-fuzzy supervision can help in improving the estimation performance in such situations. In this context, we also discuss how evolutionary optimization strategies can be employed to automatically learn the free parameters of such neuro-fuzzy supervisors.
This chapter is based on:
1) “A neuro-fuzzy assisted extended Kalman filter-based approach for Simultaneous Localization and Mapping (SLAM) problems,” by Amitava Chatterjee and Fumitoshi Matsuno, which appeared in IEEE Transactions on Fuzzy Systems, vol. 15, issue 5, pp. 984-997, October 2007. © 2007 IEEE and
2) Amitava Chatterjee, “Differential evolution tuned fuzzy supervisor adapted extended kalman filtering for SLAM problems in mobile robots,” Robotica, vol. 27, issue 3, pp. 411-423, May 2009, reproduced with permission from Cambridge University Press.
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Keywords
- Particle Swarm Optimization
- Mobile Robot
- Differential Evolution
- Particle Swarm Optimization Algorithm
- Extend Kalman Filter
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Chatterjee, A., Rakshit, A., Singh, N.N. (2013). Simultaneous Localization and Mapping (SLAM) in Mobile Robots. In: Vision Based Autonomous Robot Navigation. Studies in Computational Intelligence, vol 455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33965-3_7
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DOI: https://doi.org/10.1007/978-3-642-33965-3_7
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