Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning
Autonomous active recognition and unfolding of clothes using random decision forests and probabilistic planning
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DOI：
10.1109/icra.2014.6906974
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发表时间：
2014-09
期刊：
影响因子：
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通讯作者：
Andreas Doumanoglou;A. Kargakos;Tae-Kyun Kim;S. Malassiotis
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作者：
Andreas Doumanoglou;A. Kargakos;Tae-Kyun Kim;S. Malassiotis
我们提出了一种新的方法来解决使用双机械手自主识别和展开衣服物品的问题。这个问题包括从一个随机的点抓住一件物品，识别它，然后把它带到一种展开的状态。提出了一种数据驱动的基于随机决策森林的深度图像服装识别方法。我们还提出了一种方法，在估计和抓住两个关键点后，使用霍夫森林来展开一件衣服。这两种方法都被实施到POMDP框架中，允许机器人与服装进行最佳交互，同时考虑到识别和点估计过程中的不确定性。这种主动的识别和展开使我们的系统对噪声观测非常稳健。我们使用双臂机械手和Xtion深度传感器在普通尺寸的衣服上测试了我们的方法。我们实现了100%的主动识别准确率和93.3%的展开成功率，同时我们的系统运行速度比最先进的水平更快。
We present a novel approach to the problem of autonomously recognizing and unfolding articles of clothing using a dual manipulator. The problem consists of grasping an article from a random point, recognizing it and then bringing it into an unfolded state. We propose a data-driven method for clothes recognition from depth images using Random Decision Forests. We also propose a method for unfolding an article of clothing after estimating and grasping two key-points, using Hough forests. Both methods are implemented into a POMDP framework allowing the robot to interact optimally with the garments, taking into account uncertainty in the recognition and point estimation process. This active recognition and unfolding makes our system very robust to noisy observations. Our methods were tested on regular-sized clothes using a dual-arm manipulator and an Xtion depth sensor. We achieved 100% accuracy in active recognition and 93.3% unfolding success rate, while our system operates faster compared to the state of the art.