Robust Object Recognition in Unstructured Environments

One of the main goals of Robotics research is to help human beings in their daily tasks. However, physical interaction in everyday human scenarios requires robot systems endowed with rich sensory-motor skills and multisensory feedback that enables them to exhibit levels of adaptability high enough to achieve their goals. In this context, vision plays a main role since it provides rich information about the state of the environment. Although it is an active research area, there are still some challenging issues to be solved. Among them, the research presented in this paper addresses the object recognition for manipulation tasks in unstructured environments from a visual input. We have designed a mechanism that provides a

background model

covering the whole system’s peripersonal space such that the objects of interest can be always identified without any information about the system. Different objects and scene conditions have been considered to assess approach’s performance by showing robust object recognition in all the cases.