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Hybrid Filter Based Simultaneous Localization and Mapping for a Mobile Robot

  • Amir Panah
  • Karim Faez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) approach for a mobile robot to compensate for the Unscented Kalman Filter (UKF) based SLAM errors inherently caused by its linearization process. The proposed Hybrid filter consists of a Multi Layer Perceptron (MLP) for neural network and UKF which is a milestone for SLAM applications. The proposed approach, based on a Hybrid filter, has some advantages in handling a robotic system with nonlinear motions because of the learning property of the MLP neural network. The simulation results show the effectiveness of the proposed algorithm comparing with an UKF based SLAM.

Keywords

Hybrid filter SLAM MLP SLAM UKF 

References

  1. 1.
  2. 2.
    Choi, M.Y., Sakthivel, R., Chung, W.K.: Neural network aided extended Kalman filter for SLAM problem. In: IEEE International Conference on Robotics and Automation, pp. 1686–1690 (2007)Google Scholar
  3. 3.
    Cho, S.H.: Trajectory Tracking Control of a Pneumatic X-Y Tabel using Neural Network Based PID Control. Int. J. Precis. Eng. Manuf. 10(5), 37–44 (2009)CrossRefGoogle Scholar
  4. 4.
    Harb, M., Abielmona, R., Naji, K., Petriul, E.: Neural networks for environmental recognition and navigation of a mobile robot. In: IEEE International Instrumentation and Measurement Technology Conference, pp. 1123–1128 (2008)Google Scholar
  5. 5.
    Hu, Y.H., Hwang, J.N.: Handbook of Neural Network Signal Processing, pp. 3.1–3.23. CRC Press, Boca Raton (2001)Google Scholar
  6. 6.
    Julier, S.J., Uhlmann, J.K.: A New Extension of Kalman Filter to Nonlinear Systems. In: Proceedings of AeroSense: The 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Contro. (1997)Google Scholar
  7. 7.
    Kim, J.M., Kim, Y.T., Kim, S.S.: An accurate localization for mobile robot using extended Kalman filter and sensor fusion. In: IEEE International Joint Conference on Neural Networks, pp. 2928–2933 (2008)Google Scholar
  8. 8.
    Choi, K.-S., Lee, S.-G.: Enhanced SLAM for a Mobile Robot using Extended Kalman Filter and Neural Networks. International Journal Of Precision Engineering And Manufacturing 11(2), 255–264 (2010)CrossRefGoogle Scholar
  9. 9.
    Song, Q., He, Y.: Adaptive Unscented Kalman Filter for Estimation of Modelling Errors for Helicopter. In: 2009 IEEE International Conference on Robotics and Biomimetics, Guilin, China, December 19-23, 2009, pp. 2463–2467 (2009)Google Scholar
  10. 10.
    Zhan, R., Wan, J.: Neural Network-Aided Adaptive Unscented Kalman Filter for Nonlinear State Estimation. IEEE Signal Processing Letters 13(7), 445–448 (2006)CrossRefGoogle Scholar
  11. 11.
    Page, F.S.: Multiple-Opbject sensor Managment and optimization. PHD thesis, in the faculty of Engineering, Science and Mathematics School of Electronics and Computer science (June 2009)Google Scholar
  12. 12.
    Vafaeesefat, A.: Optimum Creep Feed Grinding Process Conditions for Rene 80 Supper Alloy Using Neural network. Int. J. Precis. Eng. Manuf. 10(3), 5–11 (2009)CrossRefGoogle Scholar
  13. 13.
    Zhu, J., Zheng, N., Yuan, Z., Zhang, Q., Zhang, X.: Unscented SLAM with conditional iterations. In: 2009 IEEE Intelligent Vehicles Symposium, pp. 134–139 (2009)Google Scholar
  14. 14.
    Yu, Z.-J., Dong, S.-L., Wei, J.-M., Xing, T., Liu, H.-T.: Neural Network Aided Unscented Kalman Filter for Maneuvering Target Tracking in Distributed Acoustic Sensor Networks. In: International Conference on Computing: Theory and Applications, Kolkata, India, March 5-7 (2007)Google Scholar
  15. 15.
    Zu, L., Wang, H.K., Yue, F.: Artificial neural networks for mobile robot acquiring heading angle. In: Proceedings of the Third Intemational Conference on Machine Learning and Cybernetics, pp. 26–29 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amir Panah
    • 1
    • 2
  • Karim Faez
    • 3
  1. 1.Mechatronics Research LaboratoryQazvin Islamic Azad UniversityQazvinIran
  2. 2.Young Researchers ClubQazvin Islamic Azad UniversityQazvinIran
  3. 3.Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran

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