Lab Meeting August 18, 2008 (Atwood): Laser and Vision Based Outdoor Object Mapping

Title: Laser and Vision Based Outdoor Object Mapping

Arthur: Bertrand Douillard, Dieter Fox, Fabio Ramos

Abstract:
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 77% accuracy in classifying the object types observed along a 750 meters long test trajectory.

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