From modular aided inertial navigation to distributed collaborative state estimation

In this seminar, Roland Jung will provide a brief introduction to filter-based localization methods for unnamed aerial vehicles (UAVs)  by means of an aided inertial navigation system. He will present his truly modular estimation approach that found application for scalable ultra-wideband aided inertial navigation. The estimation approach builds upon a novel state-decoupling scheme, denoted as Isolated Kalman Filtering (IsoKF), which allows for a unified filter architecture for both modular sensor fusion on a single UAV and for collaborative state estimation, i.e. multi-robot localization. In fact, the proposed IsoKF paradigm, allows to treat physical sensors as isolated estimator instances (e.g., mounted on an agent or distributed in the area of interest). This abstraction inherently supports modularity, but also allows to perform inter-agent observations efficiently, requiring only the subset of directly involved estimator instances.

Short bio

Roland Jung earned his B.Sc degree in hardware and software design in 2013 and his M.Sc degree in embedded systems design in 2015 from the University of Applied Sciences Upper Austria Hagenberg. He was a junior scientist with the Autonomous Systems group at the Austrian Institute of Technology (AIT), Vienna, from 2015 to 2017. In 2023, he obtained his Ph.D. in Computer Science at the University Klagenfurt, Austria, and is currently a  postdoctoral researcher at the Control of Networked Systems Group at the University of Klagenfurt, Austria. His research interests include distributed state estimation in the field of multi-robot localization and filter-based modular aided inertial navigation.