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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yong Wang & Changxing Geng & Guofeng Zhu & Renyuan Shen & Haiyang Gu & Wanfu Liu, 2022. "Information Perception Method for Fruit Trees Based on 2D LiDAR Sensor," Agriculture, MDPI, vol. 12(7), pages 1-15, June.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:914-:d:846349
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    Cited by:

    1. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.

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