Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications

Wagner, Nikolaus, Kirk, Raymond, Hanheide, Marc and Cielniak, Grzegorz (2021) Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications. In: IEEE International Conference on Robotics and Automation (ICRA).

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Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications
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Abstract

Recent developments in agriculture have highlighted the potential of as well as the need for the use of robotics. Various processes in this field can benefit from the proper use of state of the art technology [1], in terms of efficiency as well
as quality. One of these areas is the harvesting of ripe fruit. In order to be able to automate this process, a robotic
harvester needs to be aware of the full poses of the crop/fruit to be collected in order to perform proper path- and collision planning. The current state of the art mainly considers problems of detection and segmentation of fruit with localisation limited to the 3D position only. The reliable and real-time estimation of the respective orientations remains a mostly unaddressed problem. In this paper, we present a compact and efficient network architecture for estimating the orientation of soft fruit such as strawberries from colour and, optionally, depth images. The proposed system can be automatically trained in a realistic simulation environment. We evaluate the system’s performance on simulated datasets and validate its operation on publicly available images of strawberries to demonstrate its practical use. Depending on the amount of training data used, coverage of state space, as well as the availability of RGB-D or RGB
data only, mean errors of as low as 11° could be achieved.

Keywords:3d vision, orientation estimation, robotic harvesting
Subjects:H Engineering > H670 Robotics and Cybernetics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:44426
Deposited On:29 Apr 2021 09:38

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