鏡面・対称物体の6D姿勢推定
Pose Estimation of Specular and Symmetrical Objects
単眼RGBカメラのみを用い、データ駆動型のCNNで鏡面金属物体の6D姿勢を推定し、対称性を扱うコスト関数を提案して把持の実現可能性を示した論文。
著者: Jiaming Hu, Hongyi Ling, Priyam Parashar, Aayush Naik, Henrik Christensen
分類: cs.RO, cs.CV
原文アブストラクト
In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental light conditions making traditional methods, like template matching, fail. In the last 30 years, pose estimation of the specular object has been a consistent challenge, and most related works require massive knowledge modeling effort for light setups, environment, or the object surface. On the other hand, recent works exhibit the feasibility of 6D pose estimation on a monocular camera with convolutional neural networks(CNNs) however they mostly use opaque objects for evaluation. This paper provides a data-driven solution to estimate the 6D pose of specular objects for grasping them, proposes a cost function for handling symmetry, and demonstrates experimental results showing the system's feasibility.
