Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data

Accurate fruit tree models are essential for canopy volume measurement work, we build an orchard mobile robot platform and develop a fruit tree model reconstruction algorithm based on it, optimize the LIDAR Odometry specifically for the orchard environment, fuse the LIDAR Odometry, Inertial Measurem...

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Autores principales:	Peng Gao, Junsheng Jiang, Jian Song, Fuxiang Xie, Yang Bai, Yuesheng Fu, Zhengtao Wang, Xiang Zheng, Shengqiao Xie, Baocheng Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Orchard robot multi-sensor fusion model reconstruction point cloud processing Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fbc827a95a4742d5ba3d3cc43e0fb27a
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id	oai:doaj.org-article:fbc827a95a4742d5ba3d3cc43e0fb27a
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spelling	oai:doaj.org-article:fbc827a95a4742d5ba3d3cc43e0fb27a2021-12-01T00:01:21ZCanopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data2169-353610.1109/ACCESS.2021.3127566https://doaj.org/article/fbc827a95a4742d5ba3d3cc43e0fb27a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9612167/https://doaj.org/toc/2169-3536Accurate fruit tree models are essential for canopy volume measurement work, we build an orchard mobile robot platform and develop a fruit tree model reconstruction algorithm based on it, optimize the LIDAR Odometry specifically for the orchard environment, fuse the LIDAR Odometry, Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS) sensor information and loop closure detection in the form of factors to add factor maps for back-end optimization to reconstruct the orchard map model, use the sliding window method to process in real time The fused information and narrowed the range where the tree trunks are located for two times of line surface feature matching, and the point cloud data are processed to get the fruit tree model. In order to make the point cloud distribution of the reconstructed model uniform, the robot also needs to match a specific walking route, and use the Hough transform and K-Means clustering algorithm to extract the linear-circular-linear walking route autonomously according to the tree row arrangement. The experimental results show that the error of the map model is less than 0.160 m, and the correlation coefficient R<sup>2</sup> and root-mean-square error (RMSE) of the canopy model for volume measurement are 0.984 and 0.102 m<sup>3</sup>, respectively. The collected LiDAR data based on the robotic platform meets the requirement of fruit canopy volume calculation.Peng GaoJunsheng JiangJian SongFuxiang XieYang BaiYuesheng FuZhengtao WangXiang ZhengShengqiao XieBaocheng LiIEEEarticleOrchard robotmulti-sensor fusionmodel reconstructionpoint cloud processingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156246-156259 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Orchard robot multi-sensor fusion model reconstruction point cloud processing Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Orchard robot multi-sensor fusion model reconstruction point cloud processing Electrical engineering. Electronics. Nuclear engineering TK1-9971 Peng Gao Junsheng Jiang Jian Song Fuxiang Xie Yang Bai Yuesheng Fu Zhengtao Wang Xiang Zheng Shengqiao Xie Baocheng Li Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data
description	Accurate fruit tree models are essential for canopy volume measurement work, we build an orchard mobile robot platform and develop a fruit tree model reconstruction algorithm based on it, optimize the LIDAR Odometry specifically for the orchard environment, fuse the LIDAR Odometry, Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS) sensor information and loop closure detection in the form of factors to add factor maps for back-end optimization to reconstruct the orchard map model, use the sliding window method to process in real time The fused information and narrowed the range where the tree trunks are located for two times of line surface feature matching, and the point cloud data are processed to get the fruit tree model. In order to make the point cloud distribution of the reconstructed model uniform, the robot also needs to match a specific walking route, and use the Hough transform and K-Means clustering algorithm to extract the linear-circular-linear walking route autonomously according to the tree row arrangement. The experimental results show that the error of the map model is less than 0.160 m, and the correlation coefficient R<sup>2</sup> and root-mean-square error (RMSE) of the canopy model for volume measurement are 0.984 and 0.102 m<sup>3</sup>, respectively. The collected LiDAR data based on the robotic platform meets the requirement of fruit canopy volume calculation.
format	article
author	Peng Gao Junsheng Jiang Jian Song Fuxiang Xie Yang Bai Yuesheng Fu Zhengtao Wang Xiang Zheng Shengqiao Xie Baocheng Li
author_facet	Peng Gao Junsheng Jiang Jian Song Fuxiang Xie Yang Bai Yuesheng Fu Zhengtao Wang Xiang Zheng Shengqiao Xie Baocheng Li
author_sort	Peng Gao
title	Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data
title_short	Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data
title_full	Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data
title_fullStr	Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data
title_full_unstemmed	Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data
title_sort	canopy volume measurement of fruit trees using robotic platform loaded lidar data
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fbc827a95a4742d5ba3d3cc43e0fb27a
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_version_	1718406179860774912

Canopy Volume Measurement of Fruit Trees Using Robotic Platform Loaded LiDAR Data

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