Functional object mapping of kitchen environments

In this paper we investigate the acquisition of 3D functional object maps for indoor household environments, in particular kitchens, out of 3D point cloud data. By modeling the static objects in the world into hierarchical classes in the map, such as cupboards, tables, drawers, and kitchen appliances, we create a library of objects which a household robotic assistant can use while performing its tasks. Our method takes a complete 3D point cloud model as input, and computes an object model for it. The objects have states (such as open and closed), and the resultant model is accurate enough to use it in physics-based simulations, where the doors can be opened based on their hinge position. The model is built through a series of geometrical reasoning steps, namely: planar segmentation, cuboid decomposition, fixture recognition and interpretation (e.g. handles and knobs), and object classification based on object state information.