Grain Truck Compartment Localization Method based on Point Cloud Projection

Abstract Quality control is an essential step before grain storage. It requires the localization of grain truck compartments and guiding robotic arms to automatically sample grains. However, the diverse types of grain trucks and the variability in parking lead to difficulties in compartment localization and inaccurate measurements. To solve this problem, a rotating 3D laser scanner is proposed to scan grain trucks. After ground calibration, the XOY plane of the rotating scanned point cloud is aligned parallel to the ground. To avoid complex point cloud segmentation, grain truck point clouds are clipped using pre-defined regions of interest (ROI). Since only 2D corner points are required, this paper presents a projection-based point cloud processing method. Here, the points of the grain truck are projected onto the XOY plane and then the points of the rear and side panels of the projected compartment are extracted for line fitting. To robustly extract compartment corners, a region growing method based on density variations is proposed. Along the fitted line, the 2D corners of the rear and side panels are extracted to obtain the length and width dimensions of the compartment. Extensive tests have shown that the proposed method can accommodate various grain truck models with a corner extraction accuracy of less than 9.8 cm, making it suitable for the automated grain truck localization and measurement tasks.