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. Th...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721724000503 |
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| author | Chen Zhenyu Dou Hanjie Gao Yuanyuan Zhai Changyuan Wang Xiu Zou Wei |
| author_facet | Chen Zhenyu Dou Hanjie Gao Yuanyuan Zhai Changyuan Wang Xiu Zou Wei |
| author_sort | Chen Zhenyu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c8e7371480964d9394b97536aa78d0dc |
| institution | DOAJ |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-c8e7371480964d9394b97536aa78d0dc2025-08-20T02:54:11ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-06-0115222123110.1016/j.aiia.2024.12.003Research on an orchard row centreline multipoint autonomous navigation method based on LiDARChen Zhenyu0Dou Hanjie1Gao Yuanyuan2Zhai Changyuan3Wang Xiu4Zou Wei5College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaNational Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Corresponding author.College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaCollege of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding author at: College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, ChinaIntelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center of Intelligent Equipment for Agriculture, Beijing 100097, ChinaOrchard 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.http://www.sciencedirect.com/science/article/pii/S2589721724000503orchard equipmentLiDARorchard map constructionRow centreline navigationHeadland turn |
| spellingShingle | Chen Zhenyu Dou Hanjie Gao Yuanyuan Zhai Changyuan Wang Xiu Zou Wei Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR Artificial Intelligence in Agriculture orchard equipment LiDAR orchard map construction Row centreline navigation Headland turn |
| title | Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR |
| title_full | Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR |
| title_fullStr | Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR |
| title_full_unstemmed | Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR |
| title_short | Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR |
| title_sort | research on an orchard row centreline multipoint autonomous navigation method based on lidar |
| topic | orchard equipment LiDAR orchard map construction Row centreline navigation Headland turn |
| url | http://www.sciencedirect.com/science/article/pii/S2589721724000503 |
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