Visual Mapping and Autonomous Navigation Using AprilTags in Omnidirectional Mobile Robots: A Realistic ROS-Gazebo Simulation Framework

This paper presents a modular, high-fidelity simulation framework for the autonomous navigation of omnidirectional mobile robots using visual localization with AprilTags. The proposed system integrates realistic robot dynamics, a dual-layer path planning architecture, and interchangeable trajectory tracking controllers, all within the ROS Noetic and Gazebo simulation environment. AprilTags are employed as low-cost fiducial markers for map construction, eliminating the need for LiDAR or GPS. The architecture supports global path planning via the A* algorithm and local reactive replanning to avoid unexpected obstacles while preserving trajectory continuity. Three control strategies—Lyapunov-based, null-space Lyapunov, and proportional–integral (PI) control—are implemented and evaluated in multiple maze-like environments. Experimental results in the simulation demonstrate accurate trajectory tracking, successful visual mapping, and effective obstacle avoidance under realistic conditions.