Focus based Feature Extraction for Pallets Recognition

Abstract

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 full all requirements, dealing with every kind of occlusions and light situ-ations possible. Even some meaning noise (or meaning misunderstand-ing) is considered. A pallet model, with limited degrees of freedom, is de-scribed and, starting from it, a complete approach to pallet recognition is out-lined. In the model we dene both virtual and real corners, that are geomet-rical object 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 nal reasoning stage selects the best solution tting the model. Experimental results and performance are reported in order to demonstrate the suitability of the proposed approach. 1