This work describes the automatic segmentation of 2-dimensional indoor maps into semantic units along lines of spatial function, such as connectivity or objects used for certain tasks. Using a conceptually simple and readily extensible energy maximization framework, segmentations similar to what a human might produce are demonstrated on several real-world datasets. In addition, it is shown how the system can perform reference resolution by adding corresponding potentials to the energy function, yielding a segmentation that responds to the context of the spatial reference.

Objects are integral to a robot's understanding of space. Various tasks such as semantic mapping, pick-and-carry missions or manipulation involve interaction with objects. Previous work in the field largely builds on the assumption that the object in question starts out within the ready sensory reach of the robot. In this work we aim to relax this assumption by providing the means to perform robust and large-scale active visual object search. Presenting spatial relations that describe topological relationships between objects, we then show how to use these to create potential search actions. We introduce a method for efficiently selecting search strategies given probabilities for those relations. Finally we perform experiments to verify the feasibility of our approach.

Robots acting in complex environments need not only be aware of objects, but also of the relationships objects have with each other. This paper suggests a conceptualization of these relationships in terms of task-relevant functional distinctions, such as support, location control, protection and confinement. Being able to discern such relations in a scene will be important for robots in practical tasks; accordingly, it is demonstrated how predictive models can be trained using data from physics simulations. The resulting models are shown to be both highly predictive and intuitively reasonable.

Motivated by functional interpretations of spatial language terms, and the need for cognitively plausible and practical abstractions for mobile service robots, we present a spatial representation based on the physical support of one object by another, corresponding to the preposition “on”. A perceptual model for evaluating this relation is suggested, and experiments - simulated as well as using a real robot - are presented. We indicate how this model can be used for important tasks such as communication of spatial knowledge, abstract reasoning and learning, taking as an example direct and indirect visual search. We also demonstrate the model experimentally and show that it produces intuitively feasible results from visual scene analysis as well as synthetic distributions that can be put to a number of uses.

We describe recent work on PECAS, an architecture for intelligent robotics that supports multi-modal interaction. Categories and Subject Descriptors I.2.8 [Computing Methodologies]: artificial intelligence:

We describe a method for an autonomous robot to efficiently locate one or more distinct objects in a realistic environment using monocular vision. We demonstrate how to efficiently subdivide acquired images into interest regions for the robot to zoom in on, using receptive field cooccurrence histograms. Objects are recognized through SIFT feature matching and the positions of the objects are estimated. Assuming a 2D map of the robot's surroundings and a set of navigation nodes between which it is free to move, we show how to compute an efficient sensing plan that allows the robot's camera to cover the environment, while obeying restrictions on the different objects' maximum and minimum viewing distances. The approach has been implemented on a real robotic system and results are presented showing its practicability and the quality of the position estimates obtained.