Multi-sensor data fusion and navigation line extraction method based on discrete factor

SUN Ke; ZHANG Yanfei; GONG Jinliang

doi:10.7671/j.issn.1001-411X.202112029

Journal of South China Agricultural University > 2022 > 43(5): 92-98. > DOI: 10.7671/j.issn.1001-411X.202112029

SUN Ke, ZHANG Yanfei, GONG Jinliang. Multi-sensor data fusion and navigation line extraction method based on discrete factor[J]. Journal of South China Agricultural University, 2022, 43(5): 92-98. DOI: 10.7671/j.issn.1001-411X.202112029

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Multi-sensor data fusion and navigation line extraction method based on discrete factor

1.
School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China
2.
School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China

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Received Date: December 16, 2021
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
In view of the fuzzy and irregular shape of the path boundary in the corn field, the common field navigation line extraction algorithm will have the problem of excessive deviation in practical application of agricultural robot. In this paper, a navigation line extraction algorithm based on discrete factor fusion of camera and 3D LiDAR is proposed for the field of corn at 3rd−5th leaf stage.

Method
First, three-dimensional lidar was used to obtain corn plant point cloud data. At the same time, the green feature binary images were obtained from the images taken by the camera using the super-green algorithm and the maximum between-cluster variance method, and then the point cloud data after cluster analysis were projected onto the target bounding box in the image. A multi-sensor data fusion support model was constructed for feature recognition. Finally the acquired feature center point was fitted as the navigation baseline.

Result
The algorithm could adapt well to complex environments and had strong anti-interference ability. The average processing time of a single frame was only 95.62 ms, and the accuracy rate was as high as 95.33%.

Conclusion
The algorithm solves the problems of shifting in finding feature centroid and unreliable recognition results in traditional algorithms, and provides a reliable and real-time navigation path for the robot to walk in corn field.
- Multi-sensor,
- Discrete factor,
- Agricultural robot,
- Navigation,
- Laser point cloud,
- Data fusion

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References (24)

References

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