Abstract

Mobile robot map building is the task of generating a model of an environment from sensor data. Most existing approaches to mobile robot mapping either build topological representations or generate accurate, metric maps of an environment. In this paper we introduce relational object maps, a novel approach to building metric maps that represent individual objects such as doors or walls. We show how to extend relational Markov networks in order to reason about a hierarchy of objects and the spatial relationships between them. Markov chain Monte Carlo is used for efficient inference and to learn the parameters of the model. We show that the spatial constraints modeled by our mapping technique yield drastic improvements for labeling line segments extracted from laser range-finders. 1

Citations