Gazebo Simulation and ROS Implementation to Examine G-mapping SLAM Algorithm to Navigate Turtlebot-۳ Robot

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 TurtleBot۳ 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 TurtleBot۳ 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.

کلیدواژه ها:

SLAM ، G-mapping ، Robotic Operating System (ROS) ، Gazebo ، Turtlebot-۳ Robot