An integral task in self-service baggage check is the detection of whether pallets are added to the baggage. Pallets loaded with the baggage are mostly obscured; therefore, a fast detection method based on a multi-layer skeleton model registration is proposed to address this issue. A point cloud skeleton model and a point-line model are constructed using a 3D point cloud model to describe the characteristics of the pallet. During online detection, the designed banded feature description is used to capture the border point clouds. Moreover, the proposed point-line potential energy iterative algorithm is utilized to register the point-line model and border points as well as to realize pallet discrimination. An iterative nearest point registration based on random sampling consistency is used to achieve accurate registration and pose calculation as well as to obtain the accurate pose of the pallet. Experimental results show that the algorithm can maintain an accuracy of 94% even when 70% of the pallet point cloud data are missing. In addition, the speed of the proposed algorithm exceeds that of a typical algorithm by more than six times.