arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1703.04771cs.RO
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基于3D模型辅助粒子滤波的人形机器人视觉末端执行器跟踪

Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms

Istituto Italiano di Tecnologia(意大利技术研究院)

Claudio Fantacci, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale

摘要:

本文讨论了递归无标记估计机器人的末端执行器使用视觉观察其摄像机。这个问题被制定成贝叶斯框架,并使用顺序蒙特卡罗(SMC)过滤解决。我们使用3D渲染引擎和机器人的计算机辅助设计(CAD)原理图来虚拟地从机器人的摄像机视点创建图像。这些图像然后被用于提取信息和估计末端执行器的姿态。为此,我们开发了一种粒子滤波器,用于估计的位置和方向的机器人的末端执行器使用直方图的定向梯度(HOG)描述符,以捕获强大的特征功能的形状在摄像机和渲染图像。我们在iCub人形机器人上实现了该算法,并将其用于闭环到达场景。我们证明了跟踪是强大的杂波,允许补偿机器人运动学中的误差和伺服臂在闭环使用视觉。

英文摘要:

This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras. The problem is formulated into the Bayesian framework and addressed using Sequential Monte Carlo (SMC) filtering. We use a 3D rendering engine and Computer Aided Design (CAD) schematics of the robot to virtually create images from the robot's camera viewpoints. These images are then used to extract information and estimate the pose of the end-effector. To this aim, we developed a particle filter for estimating the position and orientation of the robot's end-effector using the Histogram of Oriented Gradient (HOG) descriptors to capture robust characteristic features of shapes in both cameras and rendered images. We implemented the algorithm on the iCub humanoid robot and employed it in a closed-loop reaching scenario. We demonstrate that the tracking is robust to clutter, allows compensating for errors in the robot kinematics and servoing the arm in closed loop using vision.

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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