Experimental Validation of Translational Single LiDAR 2D Mechanism Into Autonomous Navigation and 3D SLAM of Mobile Robots

Most autonomous navigation approaches for mobile robots rely either on 3D LiDAR sensors, which are often costly, or on horizontally mounted 2D LiDAR sensors, which do not support threedimensional mapping. This paper presents the implementation of a 3D Simultaneous Localization and Mapping system integrated with autonomous navigation in a mobile robot using a Translational Single LiDAR 2D (TSL-2D) configuration: a three-dimensional mapping mechanism based on a single vertically mounted 2D LiDAR sensor. To verify the feasibility of the proposed approach, the methodology combines the mathematical foundations, implementation and evaluation of global planning algorithms—Dijkstra, A-star (A*), and Rapidly-exploring Random Tree Star (RRT*)—within the Robot Operating System 2 (ROS 2) framework, integrating point cloud generation from vertical LiDAR sweeps. Experiments are conducted both in simulated environments using Gazebo and in real-world scenarios. The qualitative results demonstrate that the system is reliable for evaluating autonomous navigation algorithms, including local and motion planners. Quantitatively, as a specific outcome of the proposed system, RRT* produces smoother and shorter trajectories compared to A* and Dijkstra, achieving a 7.62% reduction in path length. Furthermore, in comparison to RGB-D camera-based systems reported in the literature, the TSL-2D mechanism delivers superior precision in generating 2D projected maps from the acquired point clouds.