Feature evaluation of factorized self-localization

Abstract

Localization on a digital map is a crucial point for many advanced driver assistance systems that make use of digital map data. State of the art work mainly relies on inaccurate GPS measurements, special and costly sensors or especially attributed digital maps. In contrast to that, this work presents a localization algorithm with in-vehicle available sensors and a standard navigation map. A factorized state estimation that computes position and orientation with separate features allows accurate and reliable positioning. This work thereto proposes correlation-based features that match road curvatures of digital map and sensor data for position estimation. The vehicle orientation on the digital map can then be computed by either matching an egomotion trajectory to the digital map road geometry or by using an optical lane recognition system. Sensitive parameters of this approach are thoroughly evaluated with a highly precise DGPS reference system.