Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor

Yong Wang, Changxing Geng, Guofeng Zhu, Renyuan Shen, Haiyang Gu and Wanfu Liu
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Yong Wang: Robotics and Microsystems Centre, Soochow University, Suzhou 215000, China
Changxing Geng: Robotics and Microsystems Centre, Soochow University, Suzhou 215000, China
Guofeng Zhu: Robotics and Microsystems Centre, Soochow University, Suzhou 215000, China
Renyuan Shen: Robotics and Microsystems Centre, Soochow University, Suzhou 215000, China
Haiyang Gu: Robotics and Microsystems Centre, Soochow University, Suzhou 215000, China
Wanfu Liu: Robotics and Microsystems Centre, Soochow University, Suzhou 215000, China

Agriculture, 2022, vol. 12, issue 7, 1-15

Abstract: To solve the problem of orchard environmental perception, a 2D LiDAR sensor was used to scan fruit trees on both sides of a test platform to obtain their position. Firstly, the two-dimensional iterative closest point (2D-ICP) algorithm was used to obtain the complete point cloud data of fruit trees on both sides. Then, combining the lightning connection algorithm (LAPO) and the density-based clustering algorithm (DBSCAN), a fruit tree detection method based on density-based lightning connection clustering (LAPO-DBSCAN) was proposed. After obtaining the point cloud data of fruit trees on both sides of the test platform using the 2D-ICP algorithm, the LAPO-DBSCAN algorithm was used to obtain the position of fruit trees. The experimental results show that the positive detection rate was 96.69%, the false detection rate was 3.31%, and the average processing time was 1.14 s, verifying the reliability of the algorithm. Therefore, this algorithm can be used to accurately find the position of fruit trees, meaning that it can be applied to orchard navigation in a later stage.

Keywords: point cloud registration of fruit trees; lightning attachment procedure optimization; density-based spatial clustering of applications with noise; information perception of fruit trees (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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