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Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data

Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research a...

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Autores principales: Aguiar, André Silva, Neves dos Santos, Filipe, Sobreira, Héber, Boaventura-Cunha, José, Sousa, Armando Jorge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831384/
https://www.ncbi.nlm.nih.gov/pubmed/35155589
http://dx.doi.org/10.3389/frobt.2022.832165
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author Aguiar, André Silva
Neves dos Santos, Filipe
Sobreira, Héber
Boaventura-Cunha, José
Sousa, Armando Jorge
author_facet Aguiar, André Silva
Neves dos Santos, Filipe
Sobreira, Héber
Boaventura-Cunha, José
Sousa, Armando Jorge
author_sort Aguiar, André Silva
collection PubMed
description Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.
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spelling pubmed-88313842022-02-12 Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data Aguiar, André Silva Neves dos Santos, Filipe Sobreira, Héber Boaventura-Cunha, José Sousa, Armando Jorge Front Robot AI Robotics and AI Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context. Frontiers Media S.A. 2022-01-28 /pmc/articles/PMC8831384/ /pubmed/35155589 http://dx.doi.org/10.3389/frobt.2022.832165 Text en Copyright © 2022 Aguiar, Neves dos Santos, Sobreira, Boaventura-Cunha and Sousa. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Aguiar, André Silva
Neves dos Santos, Filipe
Sobreira, Héber
Boaventura-Cunha, José
Sousa, Armando Jorge
Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data
title Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data
title_full Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data
title_fullStr Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data
title_full_unstemmed Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data
title_short Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data
title_sort localization and mapping on agriculture based on point-feature extraction and semiplanes segmentation from 3d lidar data
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831384/
https://www.ncbi.nlm.nih.gov/pubmed/35155589
http://dx.doi.org/10.3389/frobt.2022.832165
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