关键词:

Abstract: In order to realize the automatic navigation driving of field management machinery and improve the recognition accuracy of seedling belt trajectory, this paper proposed an extremum point clustering seedling zone identification algorithm based on offset compensation model. Firstly, based on the steering gear leveling unit, the seedling belt data were collected by lidar to construct a 2.5 D point cloud image, and establish an offset compensation model through mathematical modeling to obtain correction parameters. Then, the cluster feature points were obtained through the extreme value detection algorithm, and hierarchical clustering combined with offset compensation model. Finally, the least square method was used to linearly fit the obtained seedling belt cluster points to realize the reconstruction of the seedling belt trajectory. Through the Matlab simulation of point cloud information for machine-transplanted paddy rice with a forward vision range of 2 000～3 000 mm and a standard row spacing of 220 mm. When the obtained seedling belt trajectory was compared with the real seedling belt trajectory, the maximum lateral deviation was 16 mm, the maximum median deviation was 6 mm, and the calculation took 1.50 s. Compared with the slope blur clustering seedling belt recognition algorithm, the maximum axial deviation reduced by 29 mm, and the operation time reduced by 0.14 s, which improved the accuracy of trajectory coincidence and enhanced the real-time performance of the algorithm. Above results provided theoretical basis for the navigation and driving of low-injury seedlings for paddy field management machinery.

Key words: lidar, seedling belt identification, extreme value detection, offset compensation model, improved K-means mean clustering