触覚物体形状認識のための反復最近傍ラベル付き点群
Iterative Closest Labeled Point for Tactile Object Shape Recognition
触覚特徴にラベルを付けて4D点群とし、参照モデルと反復照合することで物体を認識するiCLAPを提案し、20物体の実験で単一感覚より最大18%高い認識率を示した。
著者: Shan Luo, Wenxuan Mou, Kaspar Althoefer, Hongbin Liu
分類: cs.RO
原文アブストラクト
Tactile data and kinesthetic cues are two important sensing sources in robot object recognition and are complementary to each other. In this paper, we propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to recognize objects using both tactile and kinesthetic information.The iCLAP first assigns different local tactile features with distinct label numbers. The label numbers of the tactile features together with their associated 3D positions form a 4D point cloud of the object. In this manner, the two sensing modalities are merged to form a synthesized perception of the touched object. To recognize an object, the partial 4D point cloud obtained from a number of touches iteratively matches with all the reference cloud models to identify the best fit. An extensive evaluation study with 20 real objects shows that our proposed iCLAP approach outperforms those using either of the separate sensing modalities, with a substantial recognition rate improvement of up to 18%.
