RGB-D点群の3D畳み込みによるモデルフリー物体姿勢推定
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation
生のカラー点群をボクセル化して3D畳み込みで物体の並進・回転を推定する2段階パイプラインを提案し、ロボット把持で90%超の成功率を達成した。
著者: Zhongang Cai, Cunjun Yu, Quang-Cuong Pham
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
The conventional pose estimation of a 3D object usually requires the knowledge of the 3D model of the object. Even with the recent development in convolutional neural networks (CNNs), a 3D model is often necessary in the final estimation. In this paper, we propose a two-stage pipeline that takes in raw colored point cloud data and estimates an object's translation and rotation by running 3D convolutions on voxels. The pipeline is simple yet highly accurate: translation error is reduced to the voxel resolution (around 1 cm) and rotation error is around 5 degrees. The pipeline is also put to actual robotic grasping tests where it achieves above 90% success rate for test objects. Another innovation is that a motion capture system is used to automatically label the point cloud samples which makes it possible to rapidly collect a large amount of highly accurate real data for training the neural networks.
