Real-time Extraction of Navigation Line Based on LiDAR
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    Abstract:

    In order to improve the precision of unmanned navigation path of combine harvester, a real-time extraction method of crop harvesting navigation line was proposed based on LiDAR. A point cloud data acquisition system was built, and the installation height and angle of LiDAR were obtained by plane fitting method. The transformation of point cloud data from LiDAR coordinate system to vehicle coordinate system was realized by using three-dimensional LiDAR to scan the crop point cloud data in front of the harvester and combining the attitude information fed back by IMU inertial sensor. The coordinates of the ROI were obtained based on the scanning field angle, installation height and installation angle of LiDAR, and the ROI was filtered through and statistically to remove impurities such as dust and straw powder, so as to eliminate invalid points and outliers in point cloud data. A fast recognition algorithm of navigation line based on the elevation difference of eight neighboring grids was proposed. The coordinate value in the Z-axis direction after the point cloud rasterized was taken as the detection basis, and the difference between grid and its eight neighboring grids in the Z-axis coordinate was defined as the elevation difference. The grid was traversed and compared according to the set threshold, so as to effectively extract the harvesting boundary points. The least square algorithm was used to fit the harvesting boundary points, and the harvesting navigation line was obtained. Experiments showed that the algorithm had good robustness, and can maintain high accuracy when crops were scarce and there were many weeds, among which the average error angle of forward direction was 0.872°, the lateral deviation of header was 0.104m, and the correct rate of harvesting navigation line was 93.5%, which can provide auxiliary navigation for combine harvester and improve the accuracy of unmanned driving.

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History
  • Received:June 20,2023
  • Revised:
  • Adopted:
  • Online: December 10,2023
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