Subjects: Computer Science >> Computer Application Technology submitted time 2022-04-18
Abstract：
In the self-service baggage check and sorting of civil aviation, it is an essential function to automatically detect whether pallets is added to the self-dropped baggage, but the pallets are largely obscured by the embedded baggage,which becomes a challenging problem.For this issue, a fast detection method for embedded baggage pallets based on multi-layer skeleton model registration is proposed. In order to describe the characteristics of the pallet, the point cloud skeleton model and the point-line model are constructed by the 3D point cloud model. During online detection, the designed banded feature description and extraction method is used to grab the border point cloud, and the proposed point-line potential energy iterative algorithm is used to registration the point-line model and horizontal border points. Then, point cloud iterative nearest point registration based on random sampling consistency is used to achieve accurate registration and pose calculation. As a result, the possibility of the existence of the pallet is judged. The effectiveness of the algorithm is verified by a variety of actual pallet detection experiments. A variety of typical comparative experimental results show that when the pallet point cloud is missing within 70%, the algorithm can still maintain the accuracy of 94% and the speed exceeds typical algorithm is more than 6 times.
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Peer Review Status:
Awaiting Review
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