Cargo pallets real‐time 3D positioning method based on computer vision
Deep learning recognizes cargo pallets and image processing with Kinect calculates 3D pose via RGB-D, achieving 81.02% real-time positioning success.
Published Dec 1, 20198 citationsPaper ↗
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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.