Hybridization of GNSS and On-Board Sensors for Validating the Aurora Ecosystem

This paper presents a hybrid navigation algorithm based on loose coupling of the on-board speedometer and inertial sensors of a land vehicle with a GNSS receiver. An Extended Kalman Filter estimating ten error states is used as the hybridization framework. The algorithm is developed to serve as a baseline for the evaluation of the navigation infrastructure of the Aurora ecosystem which is an Arctic test bed for autonomous vehicles and intelligent transport systems. In the experimental tests we focus on the performance of the navigation algorithm during GNSS outages. First, the tests indicate that the quality of GNSS updates has an immediate effect on how fast the position errors accumulate when GNSS becomes unavailable. Second, using low-cost sensors together with the current navigation infrastructure available at the Aurora test site, GNSS position fixes need to be obtained at intervals no longer than 4 seconds in order to maintain a 95 % horizontal positioning accuracy better than 0.2 meters. The results serve as a basis for recommendations for further development of the Aurora ecosystem, suggesting that further positioning infrastructure could be deployed for guaranteeing a navigation performance adequate for autonomous vehicles.