Cargo pallets real-time 3D positioning method based on computer vision

Abstract

In storage environment, aiming at the problem of goods positioning when picking, the pallet is firstly recognised based on deep learning. Then, algorithm of obtaining the pose of the pallet by the image processing and Kinect sensor is proposed in this study. The pallet is recognised and its selected box is obtained by deep learning. On this basis, the position and the angle of the pallet are obtained by the image processing method, and then RGB-D transforms the position and posture of the pallet into the three-dimensional (3D) coordinate for three-dimensional positioning. The experiment results show that the algorithm can obtain real-time pallet position with the success rate of 81.02%. Thus, the algorithm can meet the requirements of the efficiency and accuracy location requirements of the storage of goods when picking.

Keywords

• learning (artificial intelligence)
• image sensors
• position measurement
• image colour analysis
• computer vision
• freight handling
• goods distribution
• image recognition
• transforms
• computerised instrumentation
• rgb-d transforms
• kinect sensor
• storage environment
• computer vision
• real-time cargo pallet position method
• cargo pallet real-time three-dimensional positioning method
• cargo pallet real-time 3d positioning method
• image processing method
• deep learning
• goods positioning