Laser and vision based outdoor object mapping

Abstract

Generating rich representations of environments can significantly improve the autonomy of 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 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 model is determined via Delaunay triangulation of the laser map. Our model incorporates laser shape features, visual appearance features, structural information extracted from clusters of laser returns, and visual object detectors trained on image data sets available on the internet. 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 objects observed along a 3 kilometer trajectory.