Self-localization of Intelligent Vehicles Based on Environmental Contours

High-precision and robust self-localization is one of basic requirements for intelligent vehicles and related applications, because it provides necessary location information for path planning, behavioral decisions. In this paper a localization method based on IBEO LUX scanners, vehicle sensors and priori maps is proposed. The environmental contour features such as trees, street lamps, green belts and building outlines which are fused by the laser scanners and vehicle information are served as localization information. These features are associated with priori feature maps and the optimal vehicle position estimate is obtained by the Monte Carlo Localization framework. Experimental results show that the mean lateral error is less than 10cm and the mean longitudinal error is less than 20cm. So the localization algorithm introduced can meet the requirements of automatic driving demand.