Key Point-based Orientation Estimation of Strawberries for Robotic Fruit Picking
This addresses labor shortages in agriculture by enabling more accurate robotic harvesting, though it is incremental as it builds on existing orientation estimation techniques.
The paper tackles the problem of estimating strawberry orientation for robotic picking by introducing a key-point-based method that predicts 3D orientation from 2D images, achieving state-of-the-art performance with an average error of 8° and a 30% improvement over prior work.
Selective robotic harvesting is a promising technological solution to address labour shortages which are affecting modern agriculture in many parts of the world. For an accurate and efficient picking process, a robotic harvester requires the precise location and orientation of the fruit to effectively plan the trajectory of the end effector. The current methods for estimating fruit orientation employ either complete 3D information which typically requires registration from multiple views or rely on fully-supervised learning techniques, which require difficult-to-obtain manual annotation of the reference orientation. In this paper, we introduce a novel key-point-based fruit orientation estimation method allowing for the prediction of 3D orientation from 2D images directly. The proposed technique can work without full 3D orientation annotations but can also exploit such information for improved accuracy. We evaluate our work on two separate datasets of strawberry images obtained from real-world data collection scenarios. Our proposed method achieves state-of-the-art performance with an average error as low as $8^{\circ}$, improving predictions by $\sim30\%$ compared to previous work presented in~\cite{wagner2021efficient}. Furthermore, our method is suited for real-time robotic applications with fast inference times of $\sim30$ms.