Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms
This addresses visual servoing for humanoid robots, enabling error compensation in kinematics, but it is incremental as it adapts existing particle filter and feature extraction methods to a specific platform.
The paper tackled markerless end-effector tracking for humanoid robots by developing a particle filter using 3D model-aided visual features, demonstrating robust tracking in clutter and closed-loop reaching on the iCub robot.
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.