Estimation and navigation methods with limited information for autonomous urban driving

Abstract

Autonomous driving in dense urban areas presents an especially difficult task. First, globally localizing information, such as GPS signal, often proves to be unreliable in such areas due to signal shadowing and multipath errors. Second, the high-definition environmental maps with sufficient information for autonomous navigation require a large amount of data to be collected from these areas, significant postprocessing of this data to generate the map, and then continual maintenance of the map to account for changes in the environment. This paper addresses the issue of autonomous driving in urban environments by investigating algorithms and an architecture to enable fully functional autonomous driving with little to no reliance on map-based measurements or GPS signals. An extended Kalman filter with odometry, compass, and sparse landmark measurements as inputs is used to provide localization. Real-time detection and estimation of key roadway features are used to create an understanding of the surrounding static scene. Navigation is accomplished by a compass-based navigation control law. Experimental scene understanding results are obtained using computer vision and estimation techniques and demonstrate the ability to probabilistically infer key features of an intersection in real time. Key results from Monte Carlo studies demonstrate the proposed localization and navigation methods. These tests provide success rates of urban navigation under different environmental conditions, such as landmark density, and show that the vehicle can navigate to a goal nearly 10 km away without any external pose update at all. Field tests validate these simulated results and demonstrate that, for given test conditions, an expected range can be determined for a given success rate.