Hierarchical Spatial Model for 2D Range Data Based Room Categorization

The next generation service robots are expected to co-exist with humans in their homes. Such a mobile robot requires an efficient representation of space, which should be compact and expressive, for effective operation in real-world environments. In this paper we present a novel approach for 2D ground-plan-like laser-range-data-based room categorization that builds on a compositional hierarchical representation of space, and show how an additional abstraction layer, whose parts are formed by merging partial views of the environment followed by graph extraction, can achieve improved categorization performance. A new algorithm is presented that finds a dictionary of exemplar elements from a multi-category set, based on the affinity measure defined among pairs of elements. This algorithm is used for part selection in new layer construction. Room categorization experiments have been performed on a challenging publicly available dataset, which has been extended in this work. State-of-the-art results were obtained by achieving the most balanced performance over all categories.