Cognitive recognition under occlusion for visually guided robotic errand service

Abstract

A reliable vision system for running robotic errand service in an unstructured indoor environment such as homes is difficult to construct. Many visual challenges, such as perspective, clutter, illumination, and occlusion, need to be handled appropriately. While most of previous researches addressed these problems from the contexts of either object recognition or object searching, our proposed approach relies on a solution that combines these two as one. We are proposing a “Cognitive Recognition” System. In the proposed cognitive recognition system, information gathered from scene recognition helps deciding the next optimal perspective, and environmental parameters measurements determine the uncertainty in recognition measurements and thus the proper probability map update used in object search. We show particularly in this paper how this approach provides a practical solution to cluttered and occluded environments. And we demonstrate the results with our HomeMate Service Robot.