Performance Evaluation of the ROS Navigation Stack Using LeGO-LOAM

In recent years, autonomous mobile robots have been increasingly employed in various domains due to their ability to navigate unpredictable environments with limited human intervention. The Robot Operating System (ROS) Navigation Stack is a contributor to this development. The present research assessed the effectiveness of the Lightweight and Ground-Optimized Lidar Odometry and Mapping (LeGO-LOAM) when integrated into the ROS Navigation Stack for a skid-steering mobile robot operating in two distinct settings. Performance was benchmarked against well-established algorithms: Gmapping handling mapping and AMCL addressing localization. The mapping experiment demonstrated that both Gmapping and LeGO-LOAM generated precise occupancy grid maps, with LeGO-LOAM providing finer environmental details due to its 3D point cloud projection. In navigation tasks, the Absolute Pose Error (APE) metric revealed AMCL’s marginally superior performance in the first environment, achieving an RMSE value of 1.637 against LeGO-LOAM’s 1.696. Conversely, in the second environment, LeGO-LOAM outperformed AMCL by recording a lower RMSE of 1.976, compared to AMCL’s 3.163. These findings support LeGO-LOAM’s viability as an alternative tool suited to localization and mapping tasks within the ROS Navigation Stack and emphasize the significance of selecting algorithms tailored to specific environments to achieve optimal performance.