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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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 |
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author | Retsinas, George Efthymiou, Niki Anagnostopoulou, Dafni Maragos, Petros |
author_facet | Retsinas, George Efthymiou, Niki Anagnostopoulou, Dafni Maragos, Petros |
author_sort | Retsinas, George |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10099271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100992712023-04-14 Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds Retsinas, George Efthymiou, Niki Anagnostopoulou, Dafni Maragos, Petros Sensors (Basel) Article 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. MDPI 2023-03-29 /pmc/articles/PMC10099271/ /pubmed/37050635 http://dx.doi.org/10.3390/s23073576 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Retsinas, George Efthymiou, Niki Anagnostopoulou, Dafni Maragos, Petros Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds |
title | Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds |
title_full | Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds |
title_fullStr | Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds |
title_full_unstemmed | Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds |
title_short | Mushroom Detection and Three Dimensional Pose Estimation from Multi-View Point Clouds |
title_sort | mushroom detection and three dimensional pose estimation from multi-view point clouds |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099271/ https://www.ncbi.nlm.nih.gov/pubmed/37050635 http://dx.doi.org/10.3390/s23073576 |
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