3Dモデル支援パーティクルフィルタによるヒューマノイドロボットの視覚的エンドエフェクタ追跡
Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms
ロボットのCADモデルから生成した描画画像とカメラ画像のHOG特徴を比較するパーティクルフィルタで、マーカなしにエンドエフェクタの位置・姿勢を推定し、iCubで視覚フィードバックによるリーチングを実現した。
著者: Claudio Fantacci, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale
分類: cs.RO
原文アブストラクト
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.
