Title :
Object recognition based on depth information and associative memory
Author :
Puls, Stephan ; Schnorr, N. ; Worn, Heinz
Author_Institution :
Inst. of Process Control & Robot, Karlsruhe Inst. of Technol., Karlsruhe, Germany
Abstract :
Steady improvement of robotic systems due to developments in the realm of sensing the world enables advances towards human-robot-cooperation. In order for the robot to be reactive in its environment objects need to be identified. In this paper an approach is presented which allows identification of objects in the working area of an industrial robot. Neural Networks are used as associative memory to learn new items and efficiently recognize learned objects.
Keywords :
Hopfield neural nets; content-addressable storage; human-robot interaction; industrial robots; learning (artificial intelligence); object recognition; production engineering computing; robot vision; Hopfield nets; associative memory; depth information; human-robot-cooperation; industrial robot; neural networks; object identification; object recognition; robotic systems; Biological neural networks; Humans; Object recognition; Robot sensing systems; Service robots; Object recognition; associative memory; cognitive robotics; depth information; neural network;
Conference_Titel :
Robotic and Sensors Environments (ROSE), 2012 IEEE International Symposium on
Conference_Location :
Magdeburg
Print_ISBN :
978-1-4673-2705-3
DOI :
10.1109/ROSE.2012.6402606
