著者

小西 嘉典 服部 宏祐 橋本 学

出版者

画像センシング技術研究会

雑誌

第24回画像センシングシンポジウム

巻号頁・発行日

2018-04-09

著者

小西 嘉典 服部 宏祐 橋本 学

出版者

公益社団法人 精密工学会

雑誌

精密工学会誌 (ISSN:09120289)

巻号頁・発行日

vol.84, no.4, pp.348-355, 2018-04-05 (Released:2018-04-05)

参考文献数

24

被引用文献数

1

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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.