Iterative Closest Labeled Point for tactile object shape recognition

Luo, Shan ORCID: 0000-0003-4760-0372, Mou, Wenxuan, Althoefer, Kaspar and Liu, Hongbin (2016) Iterative Closest Labeled Point for tactile object shape recognition. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016-10-9 - 2016-10-14, Daejeon, South Korea.

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Abstract

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%.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Clinical Research
Depositing User:	Symplectic Admin
Date Deposited:	31 Aug 2018 14:43
Last Modified:	15 Mar 2024 14:41
DOI:	10.1109/iros.2016.7759485
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3025740