基于激光雷达的果园排中线多点自主导航方法研究

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Chen Zhenyu , Dou Hanjie , Gao Yuanyuan , Zhai Changyuan , Wang Xiu , Zou Wei
{"title":"基于激光雷达的果园排中线多点自主导航方法研究","authors":"Chen Zhenyu ,&nbsp;Dou Hanjie ,&nbsp;Gao Yuanyuan ,&nbsp;Zhai Changyuan ,&nbsp;Wang Xiu ,&nbsp;Zou Wei","doi":"10.1016/j.aiia.2024.12.003","DOIUrl":null,"url":null,"abstract":"<div><div>Orchard intelligent equipment must perform autonomous navigation tasks along fruit tree row centrelines and headlands according to established operational requirements. The tree canopy obstructs satellite signals, limiting the accuracy and stability of the GNSS-based autonomous navigation system. This paper presents a multipoint autonomous navigation method with the orchard row centreline navigation capabilities by integrating light detection and ranging (LiDAR) and inertial measurement unit (IMU) data. The method begins by constructing a three-dimensional (3D) point cloud map of the orchard via the LIO_SAM algorithm, and a 3D point cloud-to-two-dimensional (2D) grid map algorithm is designed. This algorithm retains the tree trunk position information from the point cloud based on tree trunk features to obtain a 2D grid map for orchard navigation, and the navigation point coordinates were calculated based on tree trunk positions. A multipoint navigation method was designed, where the system automatically determines the completion status of the previous navigation point and sequentially issues navigation point coordinates, enabling autonomous navigation along the row centrelines and headlands during orchard operations. Row centreline navigation tests and headland turning tests were conducted, and the performances of 16-line and 32-line LiDAR with this method are compared. The research results reveal that the multipoint navigation method could achieve movement along orchard row centrelines and deploy autonomous turning. The 32-line LiDAR data demonstrated an average absolute lateral deviation of 1.83 cm, a standard deviation of 1.60 cm, and a maximum deviation of 10.30 cm at a 3-m navigation point interval, indicating greater precision. However, the turning time was longer, with increases of 8.11 % and 6.13 % with the two different turning methods compared to the 16-line LiDAR. The research results provide support for research on autonomous navigation technology for intelligent orchard equipment.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 221-231"},"PeriodicalIF":8.2000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR\",\"authors\":\"Chen Zhenyu ,&nbsp;Dou Hanjie ,&nbsp;Gao Yuanyuan ,&nbsp;Zhai Changyuan ,&nbsp;Wang Xiu ,&nbsp;Zou Wei\",\"doi\":\"10.1016/j.aiia.2024.12.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Orchard intelligent equipment must perform autonomous navigation tasks along fruit tree row centrelines and headlands according to established operational requirements. The tree canopy obstructs satellite signals, limiting the accuracy and stability of the GNSS-based autonomous navigation system. This paper presents a multipoint autonomous navigation method with the orchard row centreline navigation capabilities by integrating light detection and ranging (LiDAR) and inertial measurement unit (IMU) data. The method begins by constructing a three-dimensional (3D) point cloud map of the orchard via the LIO_SAM algorithm, and a 3D point cloud-to-two-dimensional (2D) grid map algorithm is designed. This algorithm retains the tree trunk position information from the point cloud based on tree trunk features to obtain a 2D grid map for orchard navigation, and the navigation point coordinates were calculated based on tree trunk positions. A multipoint navigation method was designed, where the system automatically determines the completion status of the previous navigation point and sequentially issues navigation point coordinates, enabling autonomous navigation along the row centrelines and headlands during orchard operations. Row centreline navigation tests and headland turning tests were conducted, and the performances of 16-line and 32-line LiDAR with this method are compared. The research results reveal that the multipoint navigation method could achieve movement along orchard row centrelines and deploy autonomous turning. The 32-line LiDAR data demonstrated an average absolute lateral deviation of 1.83 cm, a standard deviation of 1.60 cm, and a maximum deviation of 10.30 cm at a 3-m navigation point interval, indicating greater precision. However, the turning time was longer, with increases of 8.11 % and 6.13 % with the two different turning methods compared to the 16-line LiDAR. The research results provide support for research on autonomous navigation technology for intelligent orchard equipment.</div></div>\",\"PeriodicalId\":52814,\"journal\":{\"name\":\"Artificial Intelligence in Agriculture\",\"volume\":\"15 2\",\"pages\":\"Pages 221-231\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence in Agriculture\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589721724000503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence in Agriculture","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589721724000503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

果园智能设备必须根据既定的操作要求,沿着果树排中心线和岬角执行自主导航任务。树冠遮挡卫星信号,限制了基于gnss的自主导航系统的精度和稳定性。本文提出了一种结合光探测与测距(LiDAR)和惯性测量单元(IMU)数据,具有果园排中线导航能力的多点自主导航方法。该方法首先通过LIO_SAM算法构建果园三维(3D)点云图,并设计了三维点云到二维(2D)网格图算法。该算法根据树干特征保留点云中的树干位置信息,获得用于果园导航的二维网格图,并根据树干位置计算导航点坐标。设计了一种多点导航方法,系统自动确定前一个导航点的完成状态,并依次给出导航点坐标,实现果园作业期间沿行中心线和岬角的自主导航。通过行中线导航试验和海岬转弯试验,比较了采用该方法的16线和32线激光雷达的性能。研究结果表明,多点导航方法可以实现果园排中心线移动和自主转弯。32线激光雷达数据显示,在3 m导航点间隔内,平均绝对侧向偏差为1.83 cm,标准偏差为1.60 cm,最大偏差为10.30 cm,精度较高。与16线激光雷达相比,两种转弯方式的转弯时间分别增加了8.11%和6.13%。研究结果为智能果园设备自主导航技术的研究提供了支撑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR
Orchard intelligent equipment must perform autonomous navigation tasks along fruit tree row centrelines and headlands according to established operational requirements. The tree canopy obstructs satellite signals, limiting the accuracy and stability of the GNSS-based autonomous navigation system. This paper presents a multipoint autonomous navigation method with the orchard row centreline navigation capabilities by integrating light detection and ranging (LiDAR) and inertial measurement unit (IMU) data. The method begins by constructing a three-dimensional (3D) point cloud map of the orchard via the LIO_SAM algorithm, and a 3D point cloud-to-two-dimensional (2D) grid map algorithm is designed. This algorithm retains the tree trunk position information from the point cloud based on tree trunk features to obtain a 2D grid map for orchard navigation, and the navigation point coordinates were calculated based on tree trunk positions. A multipoint navigation method was designed, where the system automatically determines the completion status of the previous navigation point and sequentially issues navigation point coordinates, enabling autonomous navigation along the row centrelines and headlands during orchard operations. Row centreline navigation tests and headland turning tests were conducted, and the performances of 16-line and 32-line LiDAR with this method are compared. The research results reveal that the multipoint navigation method could achieve movement along orchard row centrelines and deploy autonomous turning. The 32-line LiDAR data demonstrated an average absolute lateral deviation of 1.83 cm, a standard deviation of 1.60 cm, and a maximum deviation of 10.30 cm at a 3-m navigation point interval, indicating greater precision. However, the turning time was longer, with increases of 8.11 % and 6.13 % with the two different turning methods compared to the 16-line LiDAR. The research results provide support for research on autonomous navigation technology for intelligent orchard equipment.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信