Vision-based object search in unknown human environment using object Co-occurrence Graph

Object search in unknown human environment by utilizing Object Co-occurrence Graph (OCG) is presented. In our approach, OCG which represents co-occurrence relations between objects is employed as robot´s knowledge. Since direct search that solely relies on the finding of target object in each scene may face some difficulties such as insufficient image´s resolution, illumination change, or occlusion problem, indirect search utilizing hints from large-size objects that relate to a target object may provide better solutions and can make the task become easier and faster. In this work, OCG is automatically constructed based on word-tagging images provided by millions of Internet users. Moreover, in order to illustrate the applicability of OCG, practical robot´s experiment that performs object search autonomously without prior information of environment´s layout and object locations is demonstrated. In addition, experimental result supports our assumption about the simplicity of the finding of target object, especially small-size object, in unknown environment when utilizing information from clue objects.