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Informations générales |
| Auteur |
Leitner, Jürgen; Harding, Simon; Frank, Mikhail; Förster, Alexander; Schmidhuber, Jürgen |
| Publié |
InTech Open Access Publisher, 2012
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| Abstract |
We present a combined machine learning and
computer vision approach for robots to localize objects. It
allows our iCub humanoid to quickly learn to provide
accurate 3D position estimates (in the centimetre range)
of objects seen.
Biologically inspired approaches, such as Artificial
Neural Networks (ANN) and Genetic Programming (GP),
are trained to provide these position estimates using the
two cameras and the joint encoder readings. No camera
calibration or explicit knowledge of the robot’s kinematic
model is needed.
We find that ANN and GP are not just faster and have
lower complexity than traditional techniques, but also
learn without the need for extensive calibration
procedures. In addition, the approach is localizing objects
robustly, when placed in the robot’s workspace at
arbitrary positions, even while the robot is moving its
torso, head and eyes. |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publié: 2004
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