Research Article | | Peer-Reviewed

Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments

Published in Frontiers (Volume 4, Issue 3)
Received: 1 March 2024     Accepted: 4 September 2024     Published: 23 September 2024
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Abstract

Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.

Published in Frontiers (Volume 4, Issue 3)
DOI 10.11648/j.frontiers.20240403.13
Page(s) 91-100
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Autonomous Mobile Robot, Mapping, Localization, Cartography, AMCL

References
[1] Xiang, Guofei, Songyi Dian, Ning Zhao, and Guodong Wang. 2023. "Semantic-Structure-Aware Multi-Level Information Fusion for Robust Global Orientation Optimization of Autonomous Mobile Robots" Sensors, vol. 23, no. 3, pp. 1125.
[2] H. Durrant-Whyte and T. Bailey, "Simultaneous localization and mapping: part I," in IEEE Robotics & Automation Magazine, vol. 13, no. 2, pp. 99-110, June 2006,
[3] T. Bailey and H. Durrant-Whyte, "Simultaneous localization and mapping (SLAM): part II," in IEEE Robotics & Automation Magazine, vol. 13, no. 3, pp. 108-117, Sept. 2006,
[4] C. Cadena et al., "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age," in IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309-1332, Dec. 2016,
[5] Ceriani, S., Marzorati, D., Matteucci, M. et al. "Single and Multi-Camera Simultaneous Localization and Mapping Using the Extended Kalman Filter," J Math Model Algor vol. 13, pp. 23–57 (2014).
[6] Song, W., Yang, Y., Fu, M. et al. "Critical Rays Self-Adaptive Particle Filtering SLAM," J Intell Robot Syst vol. 92, pp. 107–124 (2018).
[7] Namitha, N. & S. M., Vaitheeswaran & Jayasree, V. K. & Bharat, M. K. "Point Cloud Mapping Measurements Using Kinect RGB-D Sensor and Kinect Fusion for Visual Odometry," Procedia Computer Science. vol. 89, pp. 209-212,
[8] Navigation ROS stack [Online]. Available:
[9] Sobczak, Ł., Filus, K., Domańska, J. et al. "Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer," Sci Rep 12, no. 18815 (2022),
[10] ROS. [Online]. Available:
[11] Gazebo simulator [Online]. Available:
[12] Turner, Lisa, and Chris Sherlock. “An Introduction to Particle Filtering.” (2013).
[13] AMCL ROS Package [online]: Available:
[14] Hess, Wolfgang, Damon Kohler, Holger Rapp, and Daniel Andor. "Real-time loop closure in 2D LIDAR SLAM." In 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271-1278. IEEE, 2016.
[15] Kiss-Illés, Dániel, Cristina Barrado, and Esther Salamí. 2019. "GPS-SLAM: An Augmentation of the ORB-SLAM Algorithm" Sensors vol. 19, no. 22: 4973.
[16] Chen, Weifeng, Guangtao Shang, Aihong Ji, Chengjun Zhou, Xiyang Wang, Chonghui Xu, Zhenxiong Li, and Kai Hu. 2022. "An Overview on Visual SLAM: From Tradition to Semantic" Remote Sensing 14, no. 13: 3010.
[17] Mahmoud, Imbaby & Salama, May & Tawab, Asmaa. (2014). Particle / Kalman Filter for Efficient Robot Localization. International Journal of Computer Applications. 106. 20-27.
Cite This Article
  • APA Style

    Tola, T. A., Mi, J., Che, Y. Q. (2024). Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments. Frontiers, 4(3), 91-100. https://doi.org/10.11648/j.frontiers.20240403.13

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    ACS Style

    Tola, T. A.; Mi, J.; Che, Y. Q. Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments. Frontiers. 2024, 4(3), 91-100. doi: 10.11648/j.frontiers.20240403.13

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    AMA Style

    Tola TA, Mi J, Che YQ. Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments. Frontiers. 2024;4(3):91-100. doi: 10.11648/j.frontiers.20240403.13

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  • @article{10.11648/j.frontiers.20240403.13,
      author = {Tsegaye Alemu Tola and Jing Mi and Yan qiu Che},
      title = {Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments
    },
      journal = {Frontiers},
      volume = {4},
      number = {3},
      pages = {91-100},
      doi = {10.11648/j.frontiers.20240403.13},
      url = {https://doi.org/10.11648/j.frontiers.20240403.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.frontiers.20240403.13},
      abstract = {Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.
    },
     year = {2024}
    }
    

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    T1  - Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments
    
    AU  - Tsegaye Alemu Tola
    AU  - Jing Mi
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    AB  - Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.
    
    VL  - 4
    IS  - 3
    ER  - 

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