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Pallet Detection Based on Halcon for Warehouse Robots

机译：基于仓库机器人Halcon的托盘检测

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Pallet detection is the key step of cargo handling for warehouse robots. In order to improve the recognition rate of pallet detection due to the influence of complex background, a pallet detection method based on point cloud is proposed. In this method, time-of-flight (ToF) camera is used to collect the point cloud. ResNet50 neural network model which is provided by Halcon software is used for deep learning, and deep learning is used to extract the region of interest of pallet contour. The extracted region of interest is processed to obtain the regions of the pallet pockets, and the minimum rectangles surrounding each of the pocket regions are solved to obtain the position coordinates of the pocket centers. The experimental results show that the precision of pallet detection can reach 94.5%. This method has high recognition rate in complex background, and has reference value for the design of pallet detection system of warehouse robots.
机译：托盘检测是仓库机器人货物处理的关键步骤。 为了提高由于复杂背景的影响因托盘检测的识别率，提出了一种基于点云的托盘检测方法。 在这种方法中，飞行时间（TOF）相机用于收集点云。 Reset50由Halcon软件提供的Neural网络模型用于深度学习，深度学习用于提取托盘轮廓的兴趣区域。 处理所提取的感兴趣区域以获得托盘袋的区域，并且解决了每个凹口区域的最小矩形以获得袋中心的位置坐标。 实验结果表明，托盘检测的精度可达94.5％。 该方法具有高识别率在复杂背景中，并且具有仓库机器人托盘检测系统的设计参考值。

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