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)


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.


Hybrid filter SLAM MLP SLAM UKF 


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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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