Comparative benchmarking of 2d lidar slam algorithms with ros 2 on raspberry pi 5

Mobile robotics increasingly relies on SLAM for robust autonomous navigation. While many algorithms exist, systematic comparisons within the ROS 2 framework under real-world conditions remain limited. This study addresses this gap by benchmarking three widely used 2D LiDAR-based methods—GMapping, Hector SLAM, and Cartographer—on a wheeled mobile robot. Using both simulation and on-device experiments, we evaluate mapping accuracy, localization quality, and computational efficiency. Results show that Cartographer achieves the highest accuracy in structured environments, Hector SLAM demonstrates robustness without odometry, and GMapping performs reliably only in small-scale settings. These findings highlight trade-offs relevant to embedded deployment. The main contributions are: (i) a reproducible evaluation pipeline on ROS 2, (ii) quantitative analysis of accuracy versus resource usage on Raspberry Pi 5, and (iii) practical guidelines for algorithm selection in autonomous systems. This work advances the understanding of ROS 2-based SLAM and supports informed deployment in robotics applications.