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

Adrien Durand-Petiteville (1) , Emile Le Flecher (2) , Viviane Cadenat (2) , Thierry Sentenac (2) , Stavros Vougioukas (1)

1 UC Davis - University of California [Davis]
2 LAAS-RAP - Équipe Robotique, Action et Perception

This paper deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These latter are typical semi-structured 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 paper focuses on this problem. It presents a low cost but efficient vision-based system allowing to detect accurately, quickly and robustly the trees. It is made of four stereo cameras which 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 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 1ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.

Mots clés: Agricultural Automation; Autonomous Vehicle Navigation; RGB-D Perception; Robotics

Domaines: Automatique / Robotique; Traitement du signal et de l'image [eess.SP]

Adrien Durand-Petiteville, Emile Le Flecher, Viviane Cadenat, Thierry Sentenac, Stavros Vougioukas. Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards. IEEE Robotics and Automation Letters, 2018, 3 (4), pp.3876-3883. ⟨10.1109/LRA.2018.2857005⟩. ⟨hal-01963173⟩