无标记未知物体的人形机器人视觉伺服
Markerless visual servoing on unknown objects for humanoid robot platforms
AI总结：
本文提出了一种无标记未知物体的人形机器人视觉伺服框架，通过立体视觉计算可抓取物体的体积，利用递归贝叶斯滤波估计末端执行器的6D姿态，结合非线性约束优化问题计算目标姿态，并通过图像基于视觉伺服控制实现末端执行器的精确控制。
摘要：
为了精确地抓住一个物体，人形机器人需要对末端执行器、物体姿态和形状有良好的了解。本文提出了一种无标记未知物体的视觉伺服框架，分为四个主要部分：I) 通过立体视觉建立最小二乘问题来计算机器人手可抓取的物体体积；II) 基于序贯蒙特卡洛（SMC）滤波的递归贝叶斯滤波技术，用于在不使用标记的情况下估计机器人末端执行器的6D姿态；III) 建立非线性约束优化问题来计算关于物体的目标可抓取姿态；IV) 通过图像基于视觉伺服控制命令机器人末端执行器向目标姿态移动。我们通过大量实验在iCub人形机器人平台上验证了该方法的有效性和鲁棒性，实现了实时计算、平滑轨迹和亚像素精度。
英文摘要：
To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape. In this work we propose a framework for markerless visual servoing on unknown objects, which is divided in four main parts: I) a least-squares minimization problem is formulated to find the volume of the object graspable by the robot's hand using its stereo vision; II) a recursive Bayesian filtering technique, based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose (position and orientation) of the robot's end-effector without the use of markers; III) a nonlinear constrained optimization problem is formulated to compute the desired graspable pose about the object; IV) an image-based visual servo control commands the robot's end-effector toward the desired pose. We demonstrate effectiveness and robustness of our approach with extensive experiments on the iCub humanoid robot platform, achieving real-time computation, smooth trajectories and sub-pixel precisions.
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。
