A transformation model for vision-based navigation of agricultural robots

This paper presents a Top-view Transformation Model (TTM) for a vision-based autonomous navigation of an agricultural mobile robot. The TTM transforms images captured by an onboard camera into a virtual Top-view, eliminating perspective distortions such as the vanishing point effect and ensuring uniform pixel distribution. The transformed images are analyzed to ensure an autonomous navigation of the robot between crop rows. The navigation method involves real-time estimation of the robot's position relative to crop rows and the control low is derived from the estimated robot's heading and lateral offset for steering the robot along the crop rows. A simulated scenario has been generated in Gazebo in order to implement the developed approach using the Robot Operating System (ROS), while an evaluation on a real agricultural mobile robot has also been performed. The experimental results demonstrate the feasibility of the TTM approach and its implementation for autonomous navigation, reaching good performance.