Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Abstract

This letter deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These letter are typical semistructured environments where the dense canopy prevents from using GPS signal and embedded sensors are often preferred to localize the vehicle. To move safely in such environments, it is necessary to provide the robot the ability of detecting and localizing trees. This letter focuses on this problem. It presents a low cost but an efficient vision-based system allowing to detect accurately, quickly, and robustly the trees. It is made of four stereo cameras that provide a point cloud characterizing the environment. The key idea is to find the tree trunks by detecting their shadows, which are materialized by concavities in the obtained point cloud. In this way, branches and leaves are not taken into account, improving the detection robustness and, therefore, the navigation strategy. The method has been implemented using robot operating system (ROS) and validated using data sequences taken in several different orchards. The obtained results definitely validate the approach and its performances show that the processing time (around 1 ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.