平衡姿勢探索木を用いたRGB-D画像からの高速3次元物体位置姿勢認識

抄録

In this paper, we propose a fast and robust 6D pose estimation of objects from a RGB-D image. Our proposed method consists of two components: PCOF-MOD (multimodal perspectively cumulated orientation feature) and balanced pose tree. PCOF-MOD is based on the orientation histograms of depth gradient and surface normal vectors, those are extracted on synthesized depth images using randomized 3D pose parameters and 3D CAD data of a target object. Therefore, the model templates of PCOF-MOD explicitly handle a certain range of 3D object pose. Additionally, a large number of templates are organized into a coarse-to-fine 3D pose tree in order to accelerate 6D pose estimation. Predefined polyhedra for viewpoint sampling are prepared for each level of an image pyramid and 3D object pose trees are built so that the number of child nodes of every parent node are almost equal in each pyramid level. In the experimental evaluation of 6D object pose estimation on publicly available RGB-D image dataset, our proposed method showed higher accuracy and comparable speed in comparison with state-of-the-art techniques.