Localization of Indoor Mobile Robot Using Monte Carlo Localization Algorithm (MCL)


  • Ali Khaleel Mahmood Hay al-jamiaa, Baghdad 14001,Iraq
  • Robert Bicker Newcastle University, Address, Newcastle Upon Tyne NE1 7RU, UK


Localization, particles, importance weight, Pose, Differential drive, Kinematic, Global map.


One of the challenging issues in robotics is to give a mobile robot the ability to recognize its initial pose ( position and orientation) without any human help. In this paper, the components of a mobile robot will be described in addition to the specification of the sensor that will be used. Then, the map of the environment  will be defined since it is pre-defined and stored in the memory of the robot. After that, a localization algorithm has been designed, analysed and implemented to develop the ability of a mobile robot to  recognize its initial pose. Finally, the final results that have been taken practically will discussed. These result will be divided into two main sub-sections; the first section describes the particles distribution over the working environment and their position update over a number of iterations. Second section will shows the update in the importance weight values over a number of iterations and for three different number of particles.  


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How to Cite

Mahmood, A. K., & Bicker, R. (2016). Localization of Indoor Mobile Robot Using Monte Carlo Localization Algorithm (MCL). American Scientific Research Journal for Engineering, Technology, and Sciences, 26(1), 108–126. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/2188