安価なRGBカメラと6自由度把持計画によるロバストな物体把持
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN
高価な深度カメラの代わりに複数視点のRGB画像から3D再構成し、GQ-CNNを用いて6自由度の把持計画を行う手法を提案した論文。
著者: Yahav Avigal, Samuel Paradis, Harry Zhang
分類: cs.CV, cs.RO
原文アブストラクト
Recent consumer demand for home robots has accelerated performance of robotic grasping. However, a key component of the perception pipeline, the depth camera, is still expensive and inaccessible to most consumers. In addition, grasp planning has significantly improved recently, by leveraging large datasets and cloud robotics, and by limiting the state and action space to top-down grasps with 4 degrees of freedom (DoF). By leveraging multi-view geometry of the object using inexpensive equipment such as off-the-shelf RGB cameras and state-of-the-art algorithms such as Learn Stereo Machine (LSM\cite{kar2017learning}), the robot is able to generate more robust grasps from different angles with 6-DoF. In this paper, we present a modification of LSM to graspable objects, evaluate the grasps, and develop a 6-DoF grasp planner based on Grasp-Quality CNN (GQ-CNN\cite{mahler2017dex}) that exploits multiple camera views to plan a robust grasp, even in the absence of a possible top-down grasp.
