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An autonomous navigation method for orchard mobile robots based on octree 3D point cloud optimization

文献类型： 外文期刊

作者： Li, Hailong 1 ; Huang, Kai 2 ; Sun, Yuanhao 2 ; Lei, Xiaohui 2 ; Yuan, Quanchun 2 ; Zhang, Jinqi 4 ; Lv, Xiaolan 2 ;

作者机构： 1.Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing, Jiangsu, Peoples R China

2.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing, Jiangsu, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Modern Hort Equipment, Nanjing, Peoples R China

4.Wuxi Yue Tian YTK Agr Machinery Technol CO Ltd, Wuxi, Peoples R China

关键词： octree; autonomous navigation; 3D LiDAR; orchard mobile robot; point cloud optimization

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 15 卷

页码：

收录情况： SCI

摘要： 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.

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