Inventory System for Goods Based on Laser Radar Point Cloud Projection
Addressing the common non-standard warehouse inventory issues in logistics and storage,a cargo recognition algorithm based on the projection of LiDAR point cloud is proposed.Initially,the collected cargo point cloud data is spliced using the RANSAC(random sample consensus)and ICP(iterative closest point)algorithms,while an improved Euclidean clustering method is implemented for handling cargo mislayers.Subsequently,the cargo point cloud is pro-jected onto a two-dimensional plane,and the z-axis normal vector of the point cloud is used as feature information to form a feature grayscale image.Finally,an improved watershed algorithm is utilized for image segmentation of the fea-ture grayscale image.Through testing with a physical platform and on-site data,the algorithm achieves a cargo recog-nition accuracy of over 90%,offering a stable recognition rate compared to the image recognition method based on the deep learning YOLOv5 algorithm,and effectively avoiding the issue of reliance on large-scale public datasets.
point cloud projectionnormal vector feature grayscale imagewatershed algorithmgoods inventory