arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1902.02636cs.RO
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HRI中的商品化指向:从RGB-D图像中简单快速地检测指向手势

Commodifying Pointing in HRI: Simple and Fast Pointing Gesture Detection from RGB-D Images

Autonomy Lab, School of Computing Science, Simon Fraser University(西蒙弗雷泽大学计算机科学学院自治实验室)

Bita Azari, Angelica Lim, Richard T. Vaughan

摘要:

我们提出了一种简单的方法,并描述了使用商品RGB-D相机检测指向手势适合人机交互应用。我们利用最先进的基于深度CNN的检测器在RGB图像中找到手和脸,然后检查相应的深度通道像素以获得完整的3D指向矢量。我们测试了几种方法,估计手端点的指向矢量。该系统在商用硬件上的运行频率优于30 Hz:超过了典型RGB-D传感器的帧速率。的绝对指向精度的估计发现经验与地面实况数据从一个VICON运动捕捉系统,并建立有用的相互作用量进行比较。最后,我们展示了一个端到端的测试,其中机器人估计指向矢量与地平面相交的位置,并报告所获得的精度。我们提供源代码作为ROS节点,目的是在HRI系统中为这个通用组件提供商品实现。

英文摘要:

We present and characterize a simple method for detecting pointing gestures suitable for human-robot interaction applications using a commodity RGB-D camera. We exploit a state-of-the-art Deep CNN-based detector to find hands and faces in RGB images, then examine the corresponding depth channel pixels to obtain full 3D pointing vectors. We test several methods of estimating the hand end-point of the pointing vector. The system runs at better than 30Hz on commodity hardware: exceeding the frame rate of typical RGB-D sensors. An estimate of the absolute pointing accuracy is found empirically by comparison with ground-truth data from a VICON motion-capture system, and the useful interaction volume established. Finally, we show an end-to-end test where a robot estimates where the pointing vector intersects the ground plane, and report the accuracy obtained. We provide source code as a ROS node, with the intention of contributing a commodity implementation of this common component in HRI systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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