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@article{198640,
        author = {Atchaya H and Varshini A and Jeevitha J and Suresh Kumar MS},
        title = {IMPLEMENTATION OF SLAM USING MOBILE ROBOT IN ROS2},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9799-9805},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198640},
        abstract = {For autonomous mobile robots to effectively navigate and understand unfamiliar environments, a fundamental capability is Simultaneous Localization and Mapping (SLAM). This project focuses on the implementation of a SLAM system using a mobile robot within the Robot Operating System 2 (ROS2) framework. The primary objective is to enable the robot to construct a real-time map of its surroundings while simultaneously estimating its precise position within that environment.
To achieve accurate localization and mapping, the system integrates sensor data obtained from LiDAR along with onboard odometry. The ROS2 architecture, known for its improved communication, scalability, and real-time performance, facilitates seamless interaction between various nodes responsible for sensing, mapping, and robot control. SLAM algorithms such as Gmapping and Hector SLAM are utilized to process sensor data and generate occupancy grid maps in real time.
Visualization and monitoring are carried out using tools like RViz2, allowing users to observe the robot’s movement and map formation dynamically. The mobile robot platform, such as TurtleBot, is chosen due to its affordability, ease of integration, and strong compatibility with ROS2, making it ideal for research and educational purposes.
This project highlights the practical implementation of SLAM in modern robotics and provides a strong foundation for advanced applications such as autonomous navigation, path planning, and obstacle avoidance. Overall, the study demonstrates how ROS2-based SLAM systems significantly enhance the autonomy, adaptability, and intelligence of mobile robots.
The modular design approach allows individual components—such as mapping, localization, and sensor processing—to be independently developed, tested, and optimized. This flexibility makes the system highly scalable and adaptable to different robotic platforms and application requirements.
In addition, performance evaluation of the SLAM system is carried out based on parameters such as mapping accuracy, computational efficiency, and localization stability. The system is tested in various indoor environments with different levels of complexity to validate its effectiveness. The results demonstrate that the ROS2-based SLAM implementation provides reliable and consistent performance, even in partially unknown or changing environments, thereby reinforcing its suitability for real-world autonomous robotic applications.},
        keywords = {},
        month = {April},
        }