@article{oai:tokyo-metro-u.repo.nii.ac.jp:00007172,
 author = {Toda, Yuichiro and Kitai, Sasuga and Takesue, Naoyuki and Wada, Kazuyoshi and Kubota, Naoyuki and 戸田, 雄一郎 and 北井, 瑳佳 and 武居, 直行 and 和田, 一義 and 久保田, 直行},
 journal = {インテリジェントシステム・シンポジウム講演論文集},
 month = {Nov},
 note = {Recently, the expectation to rescue robots has been increasing much in order to perform the monitoring in disaster areas. However, there are many critical problems in rescue robots. Especially, we must improve the perceptual system of the rescue robot by using 3D measurement sensor. In this paper, we discuss the learning method of topological structure from 3D point cloud and introduce our Sokuiki sensor array system for measuring 3D distance data. Next we explain Batch-Learning Growing Neural Gas (BL-GNG) for learning the topological structure. Furthermore, we apply BL-GNG to extract the topological structure of a ladder. Finally, we show several experimental results of the proposed method., postprint},
 pages = {57--60},
 title = {物体把持のための3次元点群の位相構造の学習と特徴抽出},
 volume = {27},
 year = {2017}
}