Abstract-In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: "What do parts of the kitchen look like?", "How can a container be opened and closed?", "Where do objects of daily use belong?", "What is inside of cupboards/drawers?", etc. The semantic object maps presented in this article, which we call SOM + , extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM + is designed more thoroughly and that SOM + also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM + substantially advance those developed in [1] in that SOM + are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments. Robots that do not know where objects are have to search for them. Robots that do not know how objects look have to guess whether they have fetched the right one. Robots that do not know the articulation models of drawers and cupboards have to open them very carefully in order to not damage them. Thus, robots should store and maintain knowledge about their environment that enables them to perform their tasks more reliably and efficiently. We call the collection of this knowledge the robot's maps and consider maps to be models of the robot's operation environment that serve as information resources for better task performance. Robots build environment maps for many purposes. Most robot maps so far have been proposed for navigation. Robot maps for navigation enable robots to estimate their position in the environment, to check the reachability of the destination and to compute navigation plans. Depending on their purpose maps have to store different kinds of information in different forms. Maps might represent the occupancy of environment of 2D or 3D grid cells, they might contain landmarks or represent the topological structure of the environment. The maps might model objects of daily use, indoor, outdoor, underwater, extraterrestrial surfaces, and aerial environments.
Pangercic et al. REF , instead, investigate the representation of "semantic object maps" by means of a symbolic knowledge base (in description logic) associated to Prolog predicates (for inference).
Semantic Object Maps for robotic housework - representation, acquisition and use
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science