Continuous mapping for road map assisted localization

Robust localization is a fundamental component of autonomous vehicles. In that context essential information has to be provided by a sufficient selection of sensor observations. In case of land-based road-constrained motion a map offers significant information and assists localization with valuable information about the evolution of the kinematic vehicle states. Throughout this proposal cubic spline curves are chosen to approximate the true motion constraints, e.g. roads or tracks, and the vehicle kinematics are modelled in one-dimensional curve coordinates. The resulting map-to-vehicle relation enables a straight forward derivation of measurement update equations and offers itself to classical filter techniques. Incoming sensor measurements are used for a simultaneous vehicle localization and a local road map update around the current vehicle position. Moreover map-extrapolation is carried out to handle situations with incomplete maps. The proposed method is validated with simulations and within a real railway scenario.