Markerless visual servoing on unknown objects for humanoid robot platforms
This work addresses the challenge of markerless object manipulation for humanoid robots, which is incremental as it combines existing techniques like SMC filtering and visual servoing into a novel framework.
The authors tackled the problem of enabling humanoid robots to precisely reach for unknown objects without markers by developing a framework that integrates object volume estimation, end-effector pose tracking, grasp planning, and visual servoing, achieving real-time computation, smooth trajectories, and sub-pixel precision in experiments on the iCub platform.
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.