Describing Composite Urban Workspaces

In this paper we present an appearance-based method for augmenting maps of outdoor urban environments with higher-order, semantic labels. Our motivation is to increase the value and utility of the typically low-level representations built by contemporary SLAM algorithms. A supervised learning scheme is employed to train a set of classifiers to respond to common scene attributes given a mixture of geometric and visual scene information. The union of classifier responses yields a composite description of the local workspace. We apply our method to three large data sets