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Vol. 168, Issue 4, April 2014, pp. 287-293

 

Bullet

 

An Algorithm for Inspecting Self Check-in Airline Luggage Based on Hierarchical Clustering and Cube-fitting
 

Gao Qingji, Li Taiwen, Luo Qijun

Robotics Institute, Civil Aviation University of China, Tianjin, 300300, China

E-mail: taiwenl@126.com

 

 

Received: 7 March 2014 /Accepted: 28 April 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems

 

Abstract: Airport passengers are required to put only one baggage each time in the check-in self-service so that the baggage can be detected and identified successfully. In order to automatically get the number of baggage that had been put on the conveyor belt, dual laser rangefinders are used to scan the outer contour of luggage in this paper. The algorithm based on hierarchical clustering and cube-fitting is proposed to inspect the number and dimension of airline luggage. Firstly, the point cloud is projected to vertical direction. By the analysis of one-dimensional clustering, the number and height of luggage will be quickly computed. Secondly, the method of nearest hierarchical clustering is applied to divide the point cloud if the above cannot be distinguished. It can preferably solve the difficult issue like crossing or overlapping pieces of baggage. Finally, the point cloud is projected to the horizontal plane. By rotating point cloud based on the centre, its minimum bounding rectangle (MBR) is obtained. The length and width of luggage are got form MBR. Many experiments in different cases have been done to verify the effectiveness of the algorithm.

 

Keywords: Self-service Baggage Check-in System; Point Cloud; One-dimensional Clustering; Nearest Hierarchical Clustering; MBR.

 

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