Nowadays, robots are indispensable in industry, especially logistics industry, to replace human employees performing heavy lifting tasks. Introducing robots prevents musculoskeletal disorders that are common in ageing workforce. We designed and implemented a dual arm robot to grasp cardboard boxes of different dimensions using a hydrid force/position control. In a first step, the position of the cardboard was estimated using markers and ARtags, and an integrated camera. However this solution showed some limitation, because it is not possible to place ARtag on every cardboard of a logistic warehouse. In this paper, we propose a new method to estimate one cardboard box position based on vision without the need of markers at all. Our method explores the advantages of the RGBD integrated camera through the use of strong features and perspective geometry. Our method is very adequate to the case of one cardboard box due to the simplicity of its geometric shape. The experiments show that our method is fast, robust and precise, and of course is better suited to the logistics warehouse environment than the marker estimation procedure for palletization applications.

如今，机器人已成为行业中（尤其是物流行业）必不可少的机器人，以代替执行繁重任务的人员。引进机器人可以防止劳动力老龄化中常见的肌肉骨骼疾病。我们设计并实现了一个双臂机器人，使用液压/位置控制来抓取不同尺寸的纸板箱。第一步，使用标记和ARtag以及集成的摄像头估算纸板的位置。但是，此解决方案显示出一定的局限性，因为不可能将ARtag放置在物流仓库的每个纸板上。在本文中，我们提出了一种无需视线即可完全根据视觉估计纸箱位置的新方法。我们的方法通过使用强大的功能和透视几何图形来探索RGBD集成相机的优势。由于其几何形状的简单性，我们的方法非常适合一个纸板箱的情况。实验表明，我们的方法是快速，可靠和精确的，并且比用于码垛应用的标记估计程序更适合于物流仓库环境。