This invention discloses a method and
system for
feature extraction of multi-layer stacked
cardboard boxes based on 3D vision. Addressing the problem that existing technologies cannot accurately quantify surface anomalies and multi-dimensional stacking features of
cardboard boxes in complex stacking scenarios, this invention acquires the original
point cloud and RGB images of the stacking scene; after preprocessing, a height distribution
histogram is constructed and clustered to obtain
initial point cloud clusters; warped anomalies are eliminated through curvature analysis and normal vector consistency to obtain accurate upper
surface point clouds;
principal component analysis is used to obtain the length, width, and normal vector direction of the
cardboard box; basic geometric features, damage features,
occlusion relationships, overhang / overlap features, tilt features, and multi-layer interlacing features are extracted by combining RGB images; finally, the
feature vector of each
cardboard box is output in a structured manner. This invention achieves refined
point cloud processing, effectively eliminates warped points, systematically quantifies complex stacking features, and integrates
multimodal data, significantly improving the ability to identify and extract features from cardboard boxes in complex logistics scenarios.