Voronoi Random Fields: Extracting Topological Structure of Indoor Environments via Place Labeling

Abstract

The ability to build maps of indoor environments is extremely important for autonomous mobile robots. In this paper we introduce Voronoi random fields (VRFs), a novel technique for mapping the topological structure of indoor environments. Our maps describe environments in terms of their spatial layout along with information about the different places and their connectivity. To build these maps, we extract a Voronoi graph from an occupancy grid map generated with a laser range-finder, and then represent each point on the Voronoi graph as a node of a conditional random field, which is a discriminatively trained graphical model. The resulting VRF estimates the label of each node, integrating features from both the map and the Voronoi topology. The labels provide a segmentation of an environment, with the different segments corresponding to rooms, hallways, or doorways. Experiments using different maps show that our technique is able to label unknown environments based on parameters learned from other environments.