EgoMaster: A central ego motion estimation for driver assist systems
In this paper we present an approach for a central ego motion estimation with standard and near-series sensors for advanced driver assist systems. The two main contributions of this article are the provision of all variables relative to the road surface and the fusion of several sensors and perception modules providing information of ego localization. Thus we employ a discrete Kalman filter to consider the standard lift sensors of the suspension and to take the radial tire deflection into account. Additionally, a reference sensor set is introduced which enables to evaluate all measured and estimated states, even those which are not related to the vector of gravity. The validation by the reference sensor set demonstrates the accurrancy and the efficiency of the approach.