A 3D-robot vision system for automatic unloading of containers

Hendrik Thamer, H. Kost, Daniel Weimer, B. Scholz-Reiter
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引用次数: 4

Abstract

Unloading of standard containers within logistic processes is mainly performed manually. Amongst gripping technology, the development of a robot vision system for recognizing different shaped logistic goods is a major technical obstacle for developing robotic systems for automatic unloading of containers. Goods can be arbitrarily placed inside a container and the resulting packaging scenarios usually have a high degree of occlusion. Existing systems and approaches use range information acquired by laser scanners for recognizing and localizing goods inside of containers. They are restricted to a single shape class of goods and often have limited size ranges for goods. This paper presents a robot vision for recognizing and localizing differently shaped and sized objects in piled packaging scenarios using range data acquired by different kinds of range sensors. After a specific segmentation step, different shaped partial surfaces are detected and classified in point cloud data and combined to complete logistic goods. The system is evaluated with real and simulated sensor data from different packaging scenarios.
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用于集装箱自动卸货的3d机器人视觉系统
在物流过程中,标准集装箱的卸载主要是手工完成的。在夹持技术中,识别不同形状物流货物的机器人视觉系统的开发是开发集装箱自动卸载机器人系统的主要技术障碍。货物可以任意放置在集装箱内,由此产生的包装场景通常具有高度的遮挡。现有的系统和方法使用激光扫描仪获得的距离信息来识别和定位集装箱内的货物。他们被限制在单一形状的货物类别,往往有有限的尺寸范围的货物。本文提出了一种利用不同距离传感器获取的距离数据来识别和定位堆垛包装场景中不同形状和大小的物体的机器人视觉。经过特定的分割步骤,在点云数据中检测并分类不同形状的局部曲面,并将其组合成完整的物流货物。该系统使用来自不同封装场景的真实和模拟传感器数据进行评估。
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