Abstract
Some of object recognition approaches are very
effective in environments such as industrial settings, where the
position and orientation of object could be controlled. However, in everyday human environments, objects are not located in the same place at all times; rather, they are cluttered in such a way that some of them are visually occluded. Thus,
this paper proposes a method of robust object recognition combing
ontology and probabilistic inference. The basic idea even in
a human environment there is organizational principles that
objects are co-occurred with their related objects. This enables
a robot to recognize object dependably. To demonstrate the
benefits of the proposed approach, a case study is conducted
in a human working environment.
effective in environments such as industrial settings, where the
position and orientation of object could be controlled. However, in everyday human environments, objects are not located in the same place at all times; rather, they are cluttered in such a way that some of them are visually occluded. Thus,
this paper proposes a method of robust object recognition combing
ontology and probabilistic inference. The basic idea even in
a human environment there is organizational principles that
objects are co-occurred with their related objects. This enables
a robot to recognize object dependably. To demonstrate the
benefits of the proposed approach, a case study is conducted
in a human working environment.
| Original language | English |
|---|---|
| Number of pages | 7 |
| Publication status | Published - 2013 |
| Event | 2013 IEEE International Conference on Robotics and Automation - Kongresszentrum Karlsruhe, Karlsruhe, Germany Duration: 6 May 2013 → 10 May 2013 |
Workshop
| Workshop | 2013 IEEE International Conference on Robotics and Automation |
|---|---|
| Abbreviated title | ICRA 2013 |
| Country/Territory | Germany |
| City | Karlsruhe |
| Period | 6/05/13 → 10/05/13 |