RFID-based pose perception can enable industrial automation applications such as industrial robot grasping. In this paper, a RFID pose estimation method based on classification algorithm and phase-position transformation model for moving objects is proposed, which converts the traditional pose estimation problem into a machine learning classification problem by dividing the direction angle value domain of the object into several classes. The phase information of multiple RFID tags attached to the object is transformed into position information using an unwrapped phase-position model, on which the input features of the classifier is constructed. A classifier based on the LightGBM framework is constructed and trained to realize the mapping between RFID phase information and the object's pose. Extensive experiments demonstrate that the proposed method in this paper can accurately estimate the pose of moving objects in real time and successfully complete the robot grasping task for objects on the conveyor belt.

中文翻译：

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使用分类和相位变换进行基于 RFID 的运动物体姿态估计


基于 RFID 的姿态感知可以实现工业自动化应用，例如工业机器人抓取。本文提出一种基于分类算法和相位位置变换模型的运动物体RFID姿态估计方法，通过将物体的方向角值域划分为若干个，将传统的姿态估计问题转化为机器学习分类问题。类。使用展开的相位位置模型将附着在物体上的多个 RFID 标签的相位信息转换为位置信息，并在此基础上构建分类器的输入特征。构建并训练基于LightGBM框架的分类器，实现RFID相位信息与物体位姿的映射。大量实验表明，本文提出的方法能够实时准确估计运动物体的位姿，成功完成机器人对传送带上物体的抓取任务。