An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization
Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processin...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1510683/full |
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| author | Hailong Li Hailong Li Hailong Li Kai Huang Kai Huang Yuanhao Sun Yuanhao Sun Xiaohui Lei Xiaohui Lei Quanchun Yuan Quanchun Yuan Jinqi Zhang Xiaolan Lv Xiaolan Lv |
| author_facet | Hailong Li Hailong Li Hailong Li Kai Huang Kai Huang Yuanhao Sun Yuanhao Sun Xiaohui Lei Xiaohui Lei Quanchun Yuan Quanchun Yuan Jinqi Zhang Xiaolan Lv Xiaolan Lv |
| author_sort | Hailong Li |
| collection | DOAJ |
| description | Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots. This approach includes two key components: 1) In terms of orchard mapping, the spatial indexing and segmentation features of the octree data structure are introduced. According to the sparsity and density of the point cloud, the 3D orchard map is adaptively divided and the key information of the orchard is retained. 2) In terms of path planning, by using octree nodes as the unit nodes for RRT* random tree expansion, an improved RRT* algorithm based on octree is proposed. Field experiments were conducted in a pear orchard based on this method. The experimental results show that: 1) The overall number of point cloud data points in the map was reduced by approximately 76.32%, while important features, including tree morphology, trellis structure, and road surface information, were fully preserved. 2) When different octree node resolutions were applied, the improved RRT* algorithm demonstrated significant improvements in path generation time, sampling point utilization, path length, and curvature. The lateral tracking error increased as the resolution of octree nodes decreased. At a resolution of 0.20 m, the maximum average lateral tracking error was 0.079 m, indicating strong path trackability. This method exhibits tremendous potential for processing large-scale 3D point cloud data and enhancing path planning efficiency, providing a valuable technical reference for the real-time autonomous navigation of mobile robots in complex orchard environments. |
| format | Article |
| id | doaj-art-b1c77b08b5fa47ca94a09c58653db6af |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-b1c77b08b5fa47ca94a09c58653db6af2025-01-07T05:24:08ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15106831510683An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimizationHailong Li0Hailong Li1Hailong Li2Kai Huang3Kai Huang4Yuanhao Sun5Yuanhao Sun6Xiaohui Lei7Xiaohui Lei8Quanchun Yuan9Quanchun Yuan10Jinqi Zhang11Xiaolan Lv12Xiaolan Lv13School of Automation, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, ChinaInstitute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, ChinaKey Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, ChinaInstitute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, ChinaKey Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, ChinaInstitute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, ChinaKey Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, ChinaInstitute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, ChinaKey Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, ChinaInstitute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, ChinaKey Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, ChinaWuxi Yue Tian (YTK) Agricultural Machinery Technology CO., Ltd., Wuxi, ChinaInstitute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, ChinaKey Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing, ChinaThree-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots. This approach includes two key components: 1) In terms of orchard mapping, the spatial indexing and segmentation features of the octree data structure are introduced. According to the sparsity and density of the point cloud, the 3D orchard map is adaptively divided and the key information of the orchard is retained. 2) In terms of path planning, by using octree nodes as the unit nodes for RRT* random tree expansion, an improved RRT* algorithm based on octree is proposed. Field experiments were conducted in a pear orchard based on this method. The experimental results show that: 1) The overall number of point cloud data points in the map was reduced by approximately 76.32%, while important features, including tree morphology, trellis structure, and road surface information, were fully preserved. 2) When different octree node resolutions were applied, the improved RRT* algorithm demonstrated significant improvements in path generation time, sampling point utilization, path length, and curvature. The lateral tracking error increased as the resolution of octree nodes decreased. At a resolution of 0.20 m, the maximum average lateral tracking error was 0.079 m, indicating strong path trackability. This method exhibits tremendous potential for processing large-scale 3D point cloud data and enhancing path planning efficiency, providing a valuable technical reference for the real-time autonomous navigation of mobile robots in complex orchard environments.https://www.frontiersin.org/articles/10.3389/fpls.2024.1510683/fulloctreeautonomous navigation3D LiDARorchard mobile robotpoint cloud optimization |
| spellingShingle | Hailong Li Hailong Li Hailong Li Kai Huang Kai Huang Yuanhao Sun Yuanhao Sun Xiaohui Lei Xiaohui Lei Quanchun Yuan Quanchun Yuan Jinqi Zhang Xiaolan Lv Xiaolan Lv An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization Frontiers in Plant Science octree autonomous navigation 3D LiDAR orchard mobile robot point cloud optimization |
| title | An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization |
| title_full | An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization |
| title_fullStr | An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization |
| title_full_unstemmed | An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization |
| title_short | An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization |
| title_sort | autonomous navigation method for orchard mobile robots based on octree 3d point cloud optimization |
| topic | octree autonomous navigation 3D LiDAR orchard mobile robot point cloud optimization |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1510683/full |
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