This study investigates both Gazebo-based simulations and real-world implementations of the Gmapping Simultaneous Localization and Mapping (SLAM) algorithm using the Robot Operating System (ROS) framework for autonomous navigation of a TurtleBot3 mobile robot. The primary objective is to evaluate the performance, robustness, and adaptability of Gmapping under different navigation strategies and environmental conditions. To this end, the SLAM_Gmapping package, a widely adopted ROS-based implementation of grid-based SLAM, is employed in combination with a two-dimensional Light Detection and Ranging (LiDAR) sensor to estimate the robot pose and construct an occupancy-grid representation of the surrounding environment. The performance of the proposed navigation framework is assessed in both simulated and physical indoor environments. Two navigation strategies are considered: manual teleoperation and autonomous navigation based on the Dynamic Window Approach (DWA). Their performance is comparatively examined in the presence of both static and dynamic obstacles in order to investigate the influence of navigation behavior on mapping accuracy, computational efficiency, and mapping time. The experimental results reveal noticeable differences between simulation and real-world operation. In the Gazebo environment, manual navigation in scenarios containing static obstacles produces more accurate maps within a shorter mapping time. In contrast, during real-world experiments, autonomous navigation using DWA provides more consistent and reliable mapping performance, although a longer period is required to complete the mapping process. Furthermore, a parametric evaluation of the Gmapping algorithm under different environmental and navigation conditions demonstrates that its performance is strongly affected by robot motion, obstacle configuration, and navigation strategy. Despite these variations, the algorithm exhibits satisfactory adaptability in indoor environments and is capable of generating usable maps for TurtleBot3 navigation. The findings highlight the importance of selecting an appropriate navigation strategy when deploying SLAM algorithms and demonstrate the practical potential of the ROS-based Gmapping framework for indoor mobile robotic mapping and autonomous navigation applications.
[2]Alsadik, B., & Karam, S. (2021). The simultaneous localization and mapping (SLAM): An overview. Journal of Applied Science and Technology Trends, 2(2), 147–158. https://doi.org/10.38094/jastt204117
[3]ROBOTIS. (n.d.). TurtleBot3. ROBOTIS e-Manual. Retrieved September 10, 2026, from ROBOTIS TurtleBot3 e-Manual
[4]Aydemir, H., Tekerek, M., & Gök, M. (2021). Examining of the effect of geometric objects on SLAM performance using ROS and Gazebo. El-Cezeri Journal of Science and Engineering, 8(3), 1441–1454. https://doi.org/10.31202/ecjse.943364
[5]Spina, M. G., Gualtieri, S., & De Rango, F. (2021). Integrating ROS and Gazebo tools with a network security module to support secure autonomous robot coordination. In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021) (pp. 369–377). SciTePress. https://doi.org/10.5220/0010566303690377
[6]Norzam, W. A. S., Hawari, H. F., & Kamarudin, K. (2019). Analysis of mobile robot indoor mapping using GMapping based SLAM with different parameter. IOP Conference Series: Materials Science and Engineering, 705(1), 012037. https://doi.org/10.1088/1757-899X/705/1/012037
[7]Rojas-Fernández, M., Mújica-Vargas, D., Matuz-Cruz, M., & López-Borreguero, D. (2018). Performance comparison of 2D SLAM techniques available in ROS using a differential drive robot. In 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP) (pp. 50–58). IEEE. https://doi.org/10.1109/CONIELECOMP.2018.8327175
[8]Yang, X. (2021). SLAM and navigation of indoor robot based on ROS and lidar. Journal of Physics: Conference Series, 1748(2), 022038. https://doi.org/10.1088/1742-6596/1748/2/022038
[9]Chen, S., Zhou, W., Yang, A.-S., Chen, H., Li, B., & Wen, C.-Y. (2022). An end-to-end UAV simulation platform for visual SLAM and navigation. Aerospace, 9(2), 48. https://doi.org/10.3390/aerospace9020048
[10]Ali, W., Sheng, L., & Ahmed, W. (2021). Robot Operating System-based SLAM for a Gazebo-simulated Turtlebot2 in 2D indoor environment with Cartographer algorithm. International Journal of Mechanical and Mechatronics Engineering, 15(3), 149–157. Publication page
[11]Singandhupe, A., & La, H. M. (2019). A review of SLAM techniques and security in autonomous driving. In 2019 Third IEEE International Conference on Robotic Computing (IRC) (pp. 602–607). IEEE. https://doi.org/10.1109/IRC.2019.00122
[12]Fan, X., Wang, Y., & Zhang, Z. (2021). An evaluation of Lidar-based 2D SLAM techniques with an exploration mode. Journal of Physics: Conference Series, 1905(1), 012021. https://doi.org/10.1088/1742-6596/1905/1/012021
[13]Thrun, S. (2008). Simultaneous localization and mapping. In M. E. Jefferies & W.-K. Yeap (Eds.), Robotics and cognitive approaches to spatial mapping (pp. 13–41). Springer. https://doi.org/10.1007/978-3-540-75388-9_3
[14]Stachniss, C., Leonard, J. J., & Thrun, S. (2016). Simultaneous localization and mapping. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (2nd ed., pp. 1153–1176). Springer. https://doi.org/10.1007/978-3-319-32552-1_46
Sayyed Noorani, M. & Aghdam Shahryar, F. (2025). Gazebo Simulation and ROS Implementation to Examine G-mapping SLAM Algorithm to Navigate Turtlebot-3 Robot. Transactions on Machine Intelligence, 8(4), 196-206. https://doi.org/10.22034/tmi.2025.244865
MLA
Sayyed Noorani, M., & Aghdam Shahryar, F. "Gazebo Simulation and ROS Implementation to Examine G-mapping SLAM Algorithm to Navigate Turtlebot-3 Robot", Transactions on Machine Intelligence, 8, 4, 2025, 196-206. doi: 10.22034/tmi.2025.244865
HARVARD
Sayyed Noorani M., Aghdam Shahryar F. (2025). 'Gazebo Simulation and ROS Implementation to Examine G-mapping SLAM Algorithm to Navigate Turtlebot-3 Robot', Transactions on Machine Intelligence, 8(4), pp. 196-206. doi: 10.22034/tmi.2025.244865
CHICAGO
M. Sayyed Noorani & F. Aghdam Shahryar, "Gazebo Simulation and ROS Implementation to Examine G-mapping SLAM Algorithm to Navigate Turtlebot-3 Robot," Transactions on Machine Intelligence, 8 4 (2025): 196-206, doi: 10.22034/tmi.2025.244865
VANCOUVER
Sayyed Noorani M., Aghdam Shahryar F. Gazebo Simulation and ROS Implementation to Examine G-mapping SLAM Algorithm to Navigate Turtlebot-3 Robot. Trans. Mach. Intell. 2025;8(4):196-206. doi: 10.22034/tmi.2025.244865
