Iterative Closest Labeled Point for tactile object shape recognition

TY - CHAP

T1 - Iterative Closest Labeled Point for tactile object shape recognition

AU - Luo, Shan

AU - Mou, Wenxuan

AU - Althoefer, Kaspar

AU - Liu, Hongbin

PY - 2016/11/28

Y1 - 2016/11/28

N2 - 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%.

AB - 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%.

UR - http://www.scopus.com/inward/record.url?scp=85006476457&partnerID=8YFLogxK

U2 - 10.1109/IROS.2016.7759485

DO - 10.1109/IROS.2016.7759485

M3 - Conference paper

AN - SCOPUS:85006476457

VL - 2016-November

SP - 3137

EP - 3142

BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016

Y2 - 9 October 2016 through 14 October 2016

ER -