Automated Spatial-Semantic Modeling with Applications to Place Labeling and Informed Search

This paper presents a spatial-semantic modeling system featuringautomated learning of object-place relations from an online annotateddatabase, and the application of these relations to a variety ofreal-world tasks. The system is able to label novel scenes with placeinformation, as we demonstrate on test scenes drawn from the same sourceas our training set. We have designed our system for future enhancementof a robot platform that performs state-of-the-art object recognitionand creates object maps of realistic environments. In this context, wedemonstrate the use of spatial-semantic information to performclustering and place labeling of object maps obtained from real homes.This place information is fed back into the robot system to inform anobject search planner about likely locations of a query object. As awhole, this system represents a new level in spatial reasoning andsemantic understanding for a physical platform.