Visual Urban Navigation for Mobile Robots: Implementation in the Duckietown Environment

This paper presents a vision-based control framework for the autonomous navigation of wheeled mobile robots in city-like environments, including both straight roads and turns. The approach leverages Computer Vision techniques and OpenCV to extract lane line features and utilizes a previously established control law to compute the necessary steering commands. The proposed method enables the robot to accurately follow the lanes and seamlessly handle complex maneuvers such as consecutive turns. The framework has been rigorously validated through extensive simulations and real-world experiments using physical robots equipped with the ROS framework. Experimental evaluations were conducted at the DIAG Robotics Lab at Sapienza University of Rome, Italy, demonstrating the practicality of the proposed solution in realistic settings. This work bridges the gap between theoretical control strategies and their practical application, offering insights into vision-based navigation systems for autonomous robotics. A video demonstration of the experiments is available at https://youtu.be/tDvpwSj8X28.