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Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds

Agricultural robotics is an up and coming field which deals with the development of robotic systems able to tackle a multitude of agricultural tasks efficiently. The case of interest, in this work, is mushroom collection in industrial mushroom farms. Developing such a robot, able to select and out-r...

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Detalles Bibliográficos
Autores principales: Retsinas, George, Efthymiou, Niki, Anagnostopoulou, Dafni, Maragos, Petros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099271/
https://www.ncbi.nlm.nih.gov/pubmed/37050635
http://dx.doi.org/10.3390/s23073576
Descripción
Sumario:Agricultural robotics is an up and coming field which deals with the development of robotic systems able to tackle a multitude of agricultural tasks efficiently. The case of interest, in this work, is mushroom collection in industrial mushroom farms. Developing such a robot, able to select and out-root a mushroom, requires delicate actions that can only be conducted if a well-performing perception module exists. Specifically, one should accurately detect the 3D pose of a mushroom in order to facilitate the smooth operation of the robotic system. In this work, we develop a vision module for 3D pose estimation of mushrooms from multi-view point clouds using multiple RealSense active–stereo cameras. The main challenge is the lack of annotation data, since 3D annotation is practically infeasible on a large scale. To address this, we developed a novel pipeline for mushroom instance segmentation and template matching, where a 3D model of a mushroom is the only data available. We evaluated, quantitatively, our approach over a synthetic dataset of mushroom scenes, and we, further, validated, qualitatively, the effectiveness of our method over a set of real data, collected by different vision settings.