Generating rich representations of environments is a
fundamental
task in mobile robotics. In this paper we introduce a novel
approach to building object type maps of outdoor environments. Our
approach uses conditional random fields (CRF) to jointly classify
the laser returns in a 2D scan map into seven object types (car,
wall, tree trunk, foliage, person, grass, and other). The spatial
connectivity of the CRF is determined via Delaunay triangulation of
the laser map. Our model incorporates laser shape features, visual
appearance features, visual object detectors trained on existing
image data sets and structural information extracted from clusters
of laser returns. The parameters of the CRF are trained from
partially labeled laser and camera data collected by a car moving
through an urban environment. Our approach achieves 91% accuracy
in classifying the object types observed along a 3 kilometer long
trajectory.