Development of a New Technique in ROS for Mobile Robots Localization in Known-Based 2D Environments

Authors

  • Iyad Hatem Tishreen University
  • Mhd Ali Alshikh Khalil Tishreen University

Keywords:

Particle filters, Robot operating system, Pose estimation, Monte Carlo Localization, amcl, Localization in ROS.

Abstract

Adaptive Monte Carlo Localization (amcl) is the only standard package for mobile robots localization in Robot Operating System (ROS). In this research, a new particle filter based localization technique named general Monte Carlo Localization (gmcl) was developed  by adding three particle filter algorithms to amcl in order to improve its performance, so the new versions of ROS could be better invested in systems that depend on the knowledge of the robot’s pose.

In addition, we compared amcl and gmcl in terms of computational complexity and the ability of addressing the pose-estimation problem in a differential drive mobile robot equipped with a LiDAR sensor. The results showed that the new proposed technique outperformed amcl in the accuracy of estimating the pose when compared to the same maximal computational workload. gmcl was able to reduce the pose-error in pose tracking and also able to increase the success rate of robot’s pose detection in the two problems of global localization and kidnapped-robot.

 

Author Biographies

Iyad Hatem, Tishreen University

Associate Professor, Mechatronics Department, Faculty of Mechanical and Electrical Engineering

Mhd Ali Alshikh Khalil, Tishreen University

Postgraduate Student (Master), Mechatronics Department, Faculty of Mechanical and Electrical Engineering

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Published

2022-01-18

How to Cite

1.
حاتم ا, الشيخ خليل م. Development of a New Technique in ROS for Mobile Robots Localization in Known-Based 2D Environments. Tuj-eng [Internet]. 2022Jan.18 [cited 2024Apr.30];43(6):119-37. Available from: http://www.journal.tishreen.edu.sy/index.php/engscnc/article/view/11579

Issue

Vol. 43 No. 6 (2021): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Master student researches

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Copyright (c) 2022 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

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