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Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field
Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359182/ https://www.ncbi.nlm.nih.gov/pubmed/30669645 http://dx.doi.org/10.3390/s19020428 |
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author | Lin, Guichao Tang, Yunchao Zou, Xiangjun Xiong, Juntao Li, Jinhui |
author_facet | Lin, Guichao Tang, Yunchao Zou, Xiangjun Xiong, Juntao Li, Jinhui |
author_sort | Lin, Guichao |
collection | PubMed |
description | Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red–green–blue–depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot. |
format | Online Article Text |
id | pubmed-6359182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63591822019-02-06 Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field Lin, Guichao Tang, Yunchao Zou, Xiangjun Xiong, Juntao Li, Jinhui Sensors (Basel) Article Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red–green–blue–depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot. MDPI 2019-01-21 /pmc/articles/PMC6359182/ /pubmed/30669645 http://dx.doi.org/10.3390/s19020428 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lin, Guichao Tang, Yunchao Zou, Xiangjun Xiong, Juntao Li, Jinhui Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field |
title | Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field |
title_full | Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field |
title_fullStr | Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field |
title_full_unstemmed | Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field |
title_short | Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field |
title_sort | guava detection and pose estimation using a low-cost rgb-d sensor in the field |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359182/ https://www.ncbi.nlm.nih.gov/pubmed/30669645 http://dx.doi.org/10.3390/s19020428 |
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