Focus based Feature Extraction for Pallets Recognition

Visual recognition for object grasping is a well-known challenge for robot automation in industrial applications. A typical example is pallet recognition in industrial environment for pick-and-place automated process. The aim of vision and reasoning algorithms is to help robots in choosing the best pallets holes location. This work proposes an application-based approach, which fulfil all requirements, dealing with every kind of occlusions and light situations possible. Even some ”meaning noise” (or ”meaning misunderstanding”) is considered. A pallet model, with limited degrees of freedom, is described and, starting from it, a complete approach to pallet recognition is outlined. In the model we define both virtual and real corners, that are geometricalobject proprieties computed by different image analysis operators. Real corners are perceived by processing brightness information directly from the image, while virtual corners are inferred at a higher level of abstraction. A final reasoning stage selects the best solution fitting the model. Experimental results and performance are reported in order to demonstrate the suitability of the proposed approach.