An identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion

Abstract

In this paper, an identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion is proposed, which can accurately obtain the lateral deviation between the weeding components and the seedling rows under different soil hardness in paddy fields to avoid crushing seedlings. The proposed method first fuses the RGB images with depth images to obtain the three-dimensional point cloud of seedlings, establishes a visual calibration system to calibrate the positions of the weeding component at the limit positions in the camera coordinate system, then obtains the relative pose relationship between the camera coordinate system and the ground coordinate system based on the inertial measurement unit to solve the influence of the altitude change of the camera on the identification of the lateral correction amount, and finally calculates the lateral correction amount based on the lateral deviation model in the ground coordinate system. The experimental platforms for the visual calibration of the weeding components and the identification of the lateral correction amount were established. The experimental results showed that the mean positioning error of the weeding components was 2.766 mm, the mean identification error of the lateral correction amount did not exceed 0.22 cm, and the standard deviation of the identification error did not exceed 0.18 cm.