Implementation of simultaneous localization and mapping for TurtleBot under the ROS design framework

This work presents a comprehensive implementation of Simultaneous Localization and Mapping (SLAM) techniques on the TurtleBot robot within the Robot Operating System (ROS) framework. The study aims to advance the capabilities of the TurtleBot, a popular and cost-effective robot, by integrating hardware and software components, including laser and odometry sensors. The SLAM algorithm, specifically Gmapping, is employed for mapping while utilizing ROS visualization tools like Rviz. The robot’s simulation in Gazebo enhances testing in controlled environments. Leveraging teleoperation for data collection, the research delves into the challenges and considerations specific to SLAM on the TurtleBot platform, addressing a notable research gap. The study extends the exploration by investigating potential future enhancements and benefits, showcasing the adaptability and versatility of SLAM-integrated robotic systems. Simulation results detail the successful execution of SLAM through teleoperation, providing insights into mapping accuracy, computational performance, and the overall quality of the generated maps. The work concludes with a discussion on the distance travelled, future prospects, and the profound impact of SLAM on robotic navigation.