Advanced state estimation for navigation of automated vehicles

For the emerging topic of automated and autonomous vehicles in all major sectors, reliable and accurate state estimation for navigation of these vehicles becomes increasingly important. Inertial navigation, aided with measurements from global navigation satellite systems (GNSS), allows high-rate and low-cost estimation of position, velocity and orientation in real-time applications. As the available satellite constellations for navigation are modernized and their number is rising, usage of multi-constellation, dual-frequency and integration of correction data lead to increased accuracy, especially in areas with partial shadowing. Different coupling methods, e.g. tightly- and loosely-coupled integrations, were developed to combine inertial and GNSS measurements. Also different error estimation filters were applied to the navigation problem, and evaluated against each other. For the typical navigation task, the objective is to choose a suitable algorithm for the specific requirements of the target application, and deploy it using an appropriate implementation strategy. This contribution gives a short introduction into the field of aided inertial navigation techniques, provides useful hints for implementation, and evaluates their performance in experiments using two different railway vehicles, an autonomous maritime vessel, and an unmanned aerial quadrotor.