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

State-of-the-art robot mapping approaches are capable of acquiring impressively accurate 2D and 3D models of their environments. To the best of our knowledge few of them can acquire models of task-relevant objects. In this paper, we introduce a novel method for acquiring models of task-relevant objects from stereo images. The proposed algorithm applies methods from projective geometry and works for rectangular objects, which are, in office- and museum-like environments, the most commonly found subclass of geometric objects. The method is shown to work accurately and for a wide range of viewing angles and distances.