Perception for Outdoor Navigation

Abstract

The contract made significant progress across a broad front on the problems of computer vision for outdoor mobile robots. New algorithms were built in neural networks, range data analysis, object recognition and road finding. Perception modules were integrated into new systems (including on road and off road, notably on the new Navlab II vehicle); and there were notable programmatic events, ranging from generating two new thesis proposals to playing a major role in the 'Tiger Team', shaping the architecture for the new DARPA program in Unmanned Ground Vehicles. This report begins with a summary of the year's activities and accomplishments, in this chapter. Chapter 2, '3-D Landmark Recognition from Range Image', provides more detail on object recognition from multiple sensor locations. Chapter 3, 'Representation and Recovery of Road Geometry in YARF', discusses geometry issues in YARF, our symbolic road tracking system. The last two chapters discuss systems issues that are important in providing cues and constraints for an active vision approach to robot driving.' A Computational Model of Driving for Autonomous Vehicles', Chapter 4, introduces the complexities of reasoning for driving in traffic. The fifth and final chapter, 'Combining artificial neural networks and symbolic processing for autonomous robot guidance', shows how we combine neural nets with map data in a complete system.