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Journal Article Object Recognition Method for Industrial Intelligent Robot
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Kye Kyung Kim, Kang Sang Seung, Kim Joong Bae, Lee Jae Yeon, 도현민, 최태용, 경진호
Issue Date
한국정밀공학회지, v.30 no.9, pp.901-908
Project Code
13VC4500, Development of the control technology with sensor fusion based recognition for dual-arm work and the technology of manufacturing process with multi-ro, Kim Joong Bae
The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96%has obtained.
KSP Keywords
2D-3D, 3D Vision, Adaptive Binarization, Complex shape, DoG filter, Intelligent Robot, Object class, Object image, Object region, Recognition method, Recognition rate